NEURODEGENERATIVE DISORDERS"

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Field of the Invention

The present invention relates to a medical device and a method for the evaluation of sleep disorders in the REM phase and, in particular, neurodegenerative disorders associated with it.

Background Art

The classification of sleep in five stages was defined based on the analysis of three main polysomnography signals: electroencephalogram (commonly abbreviated as "EEG") that records the brain activity, the electrooculogram (commonly abbreviated as "EOG") that records eye movements and the electromyogram (commonly abbreviated as "EMG") that records muscle activity (usually, in polysomnography, that of the mylohyoid muscle).

Based on the analysis of electroencephalographic, electromyography and electrooculogram parameters, it was possible to classify sleep in five stages: four non-REM stages (stage 1, stage 2, stage 3, stage 4) and one REM stage. The sleep has regular alternation of non-REM and REM phases consisting of cycles of similar duration between them. In particular, the REM stage (which takes its name from the Rapid Eye Movement) is the phase in which dreams predominantly occur and is characterized by a substantial paralysis of the muscles (detectable for example by a low tone of the mentalis muscles) and a high brain activity (the brain consumes oxygen and glucose as if the subject was awake and was doing an intellectual activity).

Some individuals may suffer from certain sleep disorders such as bruxism, nocturnal enuresis, restless leg syndrome, sleepwalking, somniloquy, insomnia, hypersomnia, sleep apnea and narcolepsy.

Rapid-eye-movement sleep (REM) behaviour disorder (RBD) is a parasomnia characterised by dream enacting behaviours - e.g. shouting, punching and falling out of bed - related to unpleasant dreams with loss of normal REM-sleep atonia (Iranzo A, Molinuevo JL, Santamaria J, Serradell M, Marti MJ, Valldeoriola F, Tolosa E. "Rapid-eye-movement sleep behaviour disorder as an early marker for a neurodegenerative disorder: a descriptive study." Lancet Neurol. 2006;5(7):572-7). Several pivotal studies have shown that patients with idiopathic form of RBD (IRBD) have an increased risk of developing neurodegenerative disease as synucleinopathies associated with substantia nigra dysfunction including Parkinson's disease (PD), Dementia with Lewy body (DLB) or multiple system atrophy (MSA). The interval between onset of RBD and development of parkinsonism is variable and can be up to 50 years (Iranzo A, Lomena F, Stockner H, Valldeoriola F, Vilaseca I, Salamero M, Molinuevo JL, Serradell M, Duch J, Pavia J, Gallego J, Seppi K, Hogl B, Tolosa E, Poewe W, Santamaria J; Sleep Innsbruck Barcelona (SINBAR) group "Decreased striatal dopamine transporter uptake and substantia nigra hyper echogenicity as risk markers of synucleinopathy in patients with idiopathic rapid-eye-movement sleep behaviour disorder: a prospective study. " Lancet Neurol. 2010;9(l l):1070-7).

Recent results from three longitudinal studies have demonstrated that patients with IRBD develop Lewy body disease with the time: in the first study, parkinsonism developed in 38% of IRBD patients nearly 4 years after the diagnosis; in the second study, 28% of IRBD patients developed PD, DLB or MSA after a period of 5 years; in the last study, 45% of IRBD patients developed a defined neurodegenerative syndrome after a follow-up period of 5 year (Iranzo A, Tolosa E, Gelpi E, Molinuevo JL, Valldeoriola F, Serradell M, Sanchez- Valle R, Vilaseca I, Lomena F, Vilas D, Llado A, Gaig C, Santamaria J. "Neurodegenerative disease status and post-mortem pathology in idiopathic rapid-eye-movement sleep behaviour disorder: an observational cohort study. " Lancet Neurol. 2013;12(5):443-53). Moreover, in a very recent report, Iranzo et al. have demonstrated that of 44 patients with IRBD, 36 (82%) developed a defined neurodegenerative syndrome over a median of 10.5 years of follow-up thus proposing to abandon the term "idiopathic" for using the term "isolated" RBD.

Taken together these evidences demonstrate that IRBD represents the prodromal phase of Lewy body disorder and that with sufficient follow-up, most cases would be diagnosed with a clinical defined Lewy body disorder as PD or DLB. Indeed, IRBD is the best candidate for the study of early events and progression of prodromal phase of Lewy body disorder for testing disease-modifying strategies to slow or stop the neurodegenerative process.

The clinical diagnosis of RBD, however, is laborious and requires history of dream- enacting behaviours and video polysomnographic confirmation of increased electromyographic activity during REM sleep associated with abnormal behaviours and no cognitive complaints or other neurological diseases (Iranzo A, Santamaria J, Tolosa E. "The clinical and pathophysiological relevance of REM sleep behavior disorder in neurodegenerative diseases. " Sleep Med Rev. 2009;13(6):385-401). Moreover, the video polysomnographic confirmation of RBD must be necessarily performed in a clinical environment with high neurological expertise, limiting the use of this method for screening studies of patients suspected of developing PD. Post-mortem as well as in vivo cardiac 1231-metaiodiobenzylguanidine (MIBG) imaging studies suggest that in patients with PD exists an early cardiac post- ganglionic denervation (Palma J A, Urrestarazu E, Alegre M, Pastor MA, Valencia M, Artieda J, Iriarte J. "Cardiac autonomic impairment during sleep is linked with disease severity in Parkinson's disease. " Clin Neurophysiol. 2013; 124(6): 1163-8). Cardiac autonomic dysfunction can involve both sympathetic (SNS) and parasympathetic systems (PNS). One of the simplest non-invasive methods to study changes in cardiovascular autonomic control is by measuring the heart rate variability (HRV) which represents a promising quantitative marker of the cardiac autonomic balance.

HRV can be defined as a physiological phenomenon where the time interval between heart beats varies. It is measured by the variation in the beat-to-beat interval5 and is generally derived from mathematical analyses of intervals between normal heart beats. HRV can be analyzed by using a time-domain or frequency analyses (Stein PK, Pu Y. "Heart rate variability, sleep and sleep disorders." Sleep Med Rev. 2012;16(l):47-66). In the frequency domain, LF (low frequency) power component is indicative of SNS functioning whereas HF (high frequency) power component is indicative of PNS.

There is evidence that, during daytime, PD patients show decreased HRV values when compared with control subjects. A recent report evaluating the circadian fluctuations of HRV in untreated de novo PD patients reported significantly suppressed HRV parameters during night-time and relationship between the average 24 h HRV and PD severity (Sauvageot N, Vaillant M, Diederich NJ. "Reduced sympathetically driven heart rate variability during sleep in Parkinson 's disease: a case-control polysomnography-based study. " Mov Disord 2011;26:234-40). Even more interesting, HRV studies in PD patients have found that there is more disturbance in HRV during the night than daytime. Decreased HRV has also been observed in RBD (Busek P, Vankova J, Opavsky J, Salinger J, Nevsimalova S. "Spectral analysis of the heart rate variability in sleep." Physiol Res 2005;54:369- 76; Ferini-Strambi L, Oldani A, Zucconi M, Smirne S. "Cardiac autonomic activity during wakefulness and sleep in REM sleep behavior disorder." Sleep 1996; 19:367- 9; Lanfranchi PA, Fradette L, Gagnon JF, Colombo R, Montplaisir J. "Cardiac autonomic regulation during sleep in idiopathic REM sleep behavior disorder. " Sleep 2007;30:1019-25).

Controversial findings have been reported in PD patients with and without RBD symptoms. In a recent study, some authors compared HRV parameters between PD with and without RBD and did not find any difference between groups suggesting that further research is needed to elucidate whether the presence of RBD in PD patients could induce different patterns of cardiac autonomic impairments.

Depending on the severity of the disorder manifested, it may be required polysomnographic analysis that implies a night sleep in a clinic, or at the patient's home, and that normally is done using special devices called polygraphs. Such devices allow to simultaneously acquire several neurophysiological and cardiorespiratory signals, so in addition to electrooculogram signals (EOG), electroencephalographic signals (EEG) and electromyography signals (EMG), more signals can be acquired relating for example to the movement of the chest and abdomen for the respiratory dynamics, electrocardiogram (EKG), 0 ₂ saturation, oral- nasal airflow (thermistor/oral-nasal cannula), position of the patient, etc...

The polygraphs are therefore complex instruments, which require processing systems dedicated to manage the acquisition and processing of the recorded data. They can be divided into two categories, fixed polygraphs, consisting of recording and processing stations for ambulatory use, and portable polygraphs, which can be transported by the patient outside of the ambulatory, and then returned to the clinic and connected to a dedicated system for reading, processing, and displaying data.

Screening activity with a polysomnographic examination by means of current polygraphs would involve high costs due to the cost of polygraphs themselves, as well as the time for processing and visualization of the results obtained from the records. A survey of this type may be too expensive for the health service, with too long waiting lists.

Summary of the Invention

The object of the present invention is to overcome the problems of the prior art discussed above, and to make available a device and a method that allow to facilitate and speed up the evaluation of REM sleep behavior disorder and neurodegenerative disorders associated with it.

Another object of the present invention is to make available a portable device to perform a polysomnographic examination and simultaneously a recording of the vegetative nervous system without the need for a post-processing of data on a computer.

Still another object of the present invention is to provide a tool enabling the execution of the instrumental examination also in the home environment, and which allows a wide diffusion of the diagnostic practice even outside of highly specialized centers.

A further object of the present invention is to make available a portable device for the study of neurodegenerative disorders which can be realized at a lower cost than the devices of the prior art and which allows to reach a diagnosis more quickly.

These and other objects are achieved by the present invention by means of a device according to claim 1 and a method according to claim 6. Further characteristics are given in the respective dependent claims.

According to an embodiment of the present invention, a portable device for the evaluation of neurodegenerative disorders include sensors and/or electrodes for the acquisition of polysomnographic signals and, in addition, at least one sensor for acquiring at least a photoplethysmography signal HRV indicative of variations in heartbeat. A microcontroller is programmed to process the polysomnographic signals in digital form and for processing the signal coming from the photoplethysmography sensor. The signal from the photoplethysmography sensor is a triangular signal which is converted into a square wave by a comparator to determine the values in the microcontroller in digital form of the time intervals RR between two peaks of the heartbeat.

It is therefore possible to combine in a single device polysomnographic signals with the HRV signal indicative of variations in heartbeat to get one or more significant values for the evaluation of any neurodegenerative disorders of a subject.

The portable device advantageously comprises at least one internal and/or external memory unit to store polysomnographic signals and the HRV signal. The stored signals are thus available to be instantly viewed on a PC, a tablet, a smartphone or the like, without further processing, to immediately display one or more significant value suitable for the evaluation of any neurodegenerative disorders.

In the portable device according to the present invention, the sensors and/or electrodes for the acquisition of polysomnographic signals include electrodes for sensing at least one electrocardiographic signal EKG, and/or electrodes for detecting at least one electroencephalography signal EEG, and/or electrodes for detecting at least one electrooculogram signal EOG, and/or electrodes for detecting at least one electromyography signal EMG.

The signals detected by these sensors are brought to the input of differential amplifiers to amplify the polysomnographic signals acquired by the respective electrodes. The signals thus amplified are then brought to the input of A/D converters to convert the polysomnographic signals acquired by the electrodes in digital form. The device according to the present invention is portable, preferably wearable by a user, and is designed to carry out an simplified polysomnographic examination, unlike the classical polygraph "general purpose", resulting in a lower cost of realization.

Without the need for a post-processing of the acquired data on the computer (to filter the signals and/or to calculate the heart rate variability), the present invention allows to speed up and simplify the procedure for establishing the diagnosis of a neurodegenerative disorder such as Parkinson's disease. The analysis of the activity of the autonomic nervous system (ANS) and behavior in the REM phase, recorded by means of the device according to the present invention, can be conducted directly by visualizing the acquired signals without the need for a post-processing on a computer.

The invention further relates to a method for the evaluation of neurodegenerative disorders, comprising the steps of:

a) acquiring from a subject polysomnographic signals chosen from at least one electrocardiographic signal EKG, at least one electroencephalography signal EEG, at least one electrooculogram signal EOG, at least one electromyography signal EMG, or any combination thereof;

b) acquiring at least one HRV signal indicative of variations in heartbeat by a photoplethysmography sensor;

c) processing the HRV signal to convert it into a square wave and determining the values of RR intervals between two peaks of the heartbeat in the time domain;

d) selecting a representative set of values of RR intervals from said signal HRV acquired in step b) and processed in step c);

e) calculating from the representative set of values of RR intervals one or more significant values for the evaluation of any neurodegenerative disorders of the subject.

In the method according to the present invention there is also provided the step of processing the polysomnographic signals to convert them from an analog form to a digital form and synchronize them in time with the values of RR intervals.

Preferably, the values of polysomnographic signals, the values of the HRV signal representative of the range of values of RR intervals selected in step d) and the significant values calculated in step e) are stored in digital form in a memory unit so as to be temporally synchronized.

The storage is carried out preferably in ASCII and/or binary format, so as to be read by any external device equipped with even a minimal processing capacity of these standard formats. In particular, in order to obtain the values indicative of any neurodegenerative disorders, the values of RR intervals determined in the time domain between two peaks of the heartbeat processed in step c) of the method are converted into corresponding values in the frequency domain and separated in a band of low frequencies and in a band of high frequencies.

This allows to separate the values actually significant of possible neurodegenerative disorders from those that are less significant, unless further deep investigation which is beyond the scope of the present invention.

Starting from the values calculated in the frequency domain, the power spectral density of the values belonging to the band of low frequencies and the values belonging to the band of high frequencies are then calculated and stored separately.

Finally, the average value of the power spectral density of the values belonging to the band of low frequencies detected during the day period, the average value of the power spectral density of the values belonging to the band of low frequencies detected during night and the ratio of these average values are calculated and stored to determine at least one significant value for the evaluation of any neurodegenerative disorders of the subject.

The present invention, allows to obtain a diagnosis for fast and economical means of a low-cost device, thanks to which it is therefore possible to perform a screening activity, with targeted surveys in different population samples, entailing, however, a burden sustainable by the health service.

Brief Description of the Drawings

Further aspects and advantages of the present invention will become more apparent from the following description, given by way of illustration and not limitation, with reference to the accompanying schematic drawings, in which:

- Figure 1 is a perspective view of an embodiment of the device according to the present invention;

- Figures 2-6 are block diagrams of the main acquisition unit of the device according to the present invention;

- Figure 7 is a block diagram of the device according to the present invention;

- Figure 8 shows an example of a graphical display of the acquired signals by means of the device according to the present invention;

- Figure 9 is a data chart which shows a cut off value of S-RBD index differentiating PD-RBD patients from those with PD;

- Figure 10 is a data chart which shows the absence of a cut off value of S-RBD index differentiating PD-RBD patients from those with PD;

- Figure 11 is a flowchart which shows some steps of the method according to the present invention; and

- Figures 12 and 13 represent an alternative embodiment of a device of the present invention.

Detailed Description

With reference to Figure 1, a portable device 1 for the evaluation of neurodegenerative disorders comprises a plurality of terminals 6 for the wire connection of electrodes/sensors that are applied to a subject under examination. Each electrode/sensor is connected to the device 1 by means of a plug 5 adapted to be inserted within a corresponding terminal 6.

In Figure 1 is indicated with reference numeral 7 a terminal to connect a photoplethysmography sensor 35 (shown in Figure 6) to the device 1, in order to detect the cardiac signal HRV indicative of the heartbeat.

The device 1 further comprises at least one slot 4 for the connection of an external memory of the removable type, such as a SD, MMC card, or the like, in which are stored the data and signals detected and/or calculated by the device 1 in order to be subsequently read by other external devices. Alternatively or in combination with the storage in a removable memory card, the device 1 may also include an internal memory and at least one communication card, such as for example a USB serial communication card type and/or a Bluetooth communication card type, to transfer data to external devices such as PC or the like.

In correspondence of each terminal 6 (input/connection for the electrodes), there is an indicator light 62 which, when connecting the electrodes/sensors, emits for example red light to indicate the absence of connection, or errors or disturbances in the connection of the electrodes/sensors, such as an impedance of contact higher than 5 kQ, or green light to indicate the correct application of the electrode and/or its proper functioning.

In Figures 2A and 2B are presented some block diagrams of a subsystem which allows the device 1 to detect the EEG signals as part of polysomnography signals. In particular, according to the scheme of Figure 2A, the EEG signal can be detected by means of two electrodes 13, 14 which are applied in correspondence of the frontal and central regions of the cerebral cortex, such as Fz and Cz, namely in correspondence of the frontal median region and in correspondence of the central median region of the cerebral cortex. A reference electrode 61, positioned in a region D distant from the head of the patient or at the mastoid process (right or left), is connected to a circuit 60 for active compensation (DRL) of the so-called "antenna effect".

The signals detected by the sensors 13 and 14 are brought to the input of a differential amplifier 8 for determining the difference between the two detected signals which is then filtered by an analog bandpass filter 9 with a frequency fL equal to 0.16 Hz and fH equal to 60 Hz. The resulting filtered signal is then fed to a A/D converter 10 by means of which the amplified and filtered analog signal is converted into a digital signal. The digitized signal is then digitally filtered by a digital filter 41 which will be described in greater detail below.

Figure 2B shows a block diagram of an alternative embodiment of the subsystem of Figure 2A, in which there is provided a further electrode 62 which is adapted to be positioned at the occipital region of the cerebral cortex, such as the region Oz (occiput median). With the reference 8' is indicated a block for amplification of the differences of the signals detected by the electrodes 13, 14, and 62. In particular, the arrangement of the electrodes of the differential signal amplified by the block 8' is preferably selected according to the international criterion 10 -20. The differential signals are then amplified, filtered, converted to digital form and digitally filtered by means of the respective blocks 9', 10' and 4Γ in a similar manner to the embodiment shown in Figure 2A.

In Figure 3 a subsystem is shown for the detection of an electrooculogram signal EOG as part of polysomnographic signals. A polysomnographic signal EOG can be detected by means of two electrodes 11 and 12, respectively, positioned in correspondence of the outer canthus of the right eye and the left eye. The electrodes 11, 12 may be disposed respectively 1 cm above and 1 cm below an ideal horizontal line passing through the eye (or according to the international criterion 10-20). A differential amplifier 15 receives signals from the two electrodes 1 1, 12 for amplifying the difference between the two signals. The resulting signal passes through a low pass analog filter 16, adapted to filter the amplified signal, and through an A/D converter 17 by means of which the amplified and filtered EOG signal is converted into a digital signal. The polysomnography EOG signal is digitized and then digitally filtered by a digital filter 42 which will be described in greater detail below.

In Figure 4 is shown a diagram of a subsystem for the acquisition of electromyography signals EMG as part of polysomnographic signals. In particular, three signals are detected by means of three pairs of electrodes 21-22, 23-24 and 25- 26. The electrodes 21 and 22 are positioned in correspondence of the mylohyoid muscle (under the chin of the patient), the electrodes 23 and 24 are positioned on an upper limb (for example in the vicinity of the biceps and/or triceps), the electrodes 25 and 26 are positioned on a lower limb (e.g. in the vicinity of the tibial muscle and/or of the calf). The signals from the electrodes 21-26 are brought to the input of a differential amplifier 18 for amplifying the differences of electric potentials that are formed in the muscle during contraction. In Figure 4 is shown with the reference 18 a block of differential amplification with three differential inputs (i.e. six individual inputs). Such amplification block 18 comprises actually three separate differential amplifiers adapted to separately amplify the difference between the signals detected by each pair of electrodes 21-22, 23-24 and 25-26. So, the outputs from the amplification block 18 are actually three amplified signals (the outputs of the three distinct differential amplifiers) relative to the pairs of electrodes 21-22, 23-24 and 25-26. The resulting signals are then filtered through an analog bandpass filter 19 with frequencies fL and fH equal to 0.16 Hz and 80 Hz, respectively. Similarly to what was previously said, the reference 19 indicates a filter block comprising three separate bandpass filters.

EMG signals are amplified and filtered and then converted into digital signals by means of at least one A/D converter 20. Also in this case, for a more simple representation of the block diagram, it is indicated with the reference 20 a single block of conversion analog-to-digital comprising three distinct A/D converters. The electromyography EMG signals are digitized and then filtered by means of a digital filter 43 which will be described in more detail later in the description.

Figure 5 shows a block diagram of a subsystem of the device 1 for the acquisition of at least one electrocardiogram signal EKG. The EKG signal can be detected by means of an electrode 30 which is adapted to be positioned at the patient's heart and a reference electrode 31. The electrode 31 may, however, be omitted if it is configured, for example, the electrode 14 as a reference electrode, namely positioned at the region Cz of the cerebral cortex (Figures 2A and 2B).

The EKG signal is then amplified by means of a differential amplifier 32, filtered by means of an anti-aliasing filter 33 and converted to digital by means of an A/D converter 34. The digitized cardiac signal EKG is then digitally filtered by means of a digital filter 44.

According to the present invention, as illustrated in the diagram of Figure 6, it is also detected a HRV signal, indicating the variation of the heartbeat, using a photoplethysmography sensor 35 (PPG sensor) applicable to an ear lobe of the patient or, alternatively, the end of a finger of the hand of a subject. The sensor 35 detects the cyclic variations of pressure tone in the capillaries of the earlobe or finger, which faithfully represent the heartbeat, through the emission (by means of a photodiode) and the uptake of infrared light absorbed by the blood (by means of a phototransistor). The subsystem of the device 1 for the detection of the HRV signal comprises at least one comparator 36 for converting the sensor PPG signal in a square wave signal or, in other words, the electrical signal of triangular shape produced by the sensor 35 is converted into a square wave signal at two levels ("low" and "high") by means of the comparator 36.

From the square wave signal it is possible to derive RR intervals in the time domain by means of an interrupt system 37 performed for example by a microcontroller 38 (Figure 7). The interrupt system 37 detects transitions from low to high level of the square wave signal, so as to determine the instant of time in which such a transition has occurred. The microcontroller, by comparing two successive transitions, determines the time interval RR from which the heartbeat rate, beat-to-beat, and its variation can be obtained over time.

Figure 7 shows a block diagram of the device 1 in which it is provided a microcontroller 38 for digital processing of the received signals from the various sensors. In particular, it is indicated with the reference 39 a block analog comprising the differential amplifiers 8, 15, 18, 32, analog filters 9, 16, 19, 33 and A/D converters 10, 17, 20, 34 shown in Figures 2 -5, to acquire polysomnographic signals EEG, EOG, EMG and EKG. Inputs of block 39 are connected to the electrodes 11- 14, 21-26, 30 and 31 for the detection of the signals mentioned above, while the output signals of the block 39 are amplified, filtered and digitized in order to be ready for digital processing by the microcontroller 38.

As described above, the digitized signals are processed by the microcontroller 38 with a digital filtering indicated schematically by a block 40 comprising the various digital filters 41-44, 4Γ shown in Figures 2-5.

In particular, for the EEG signal, the digital filters 41, 4 preferably comprise at least a bandpass filter of Butterworth type with programmable frequencies fL equal to 3 Hz and fH between 30 Hz and 40 Hz.

For the EOG signal, the digital filter 42 preferably comprises a bandpass filter of Butterworth type with programmable frequencies fL, between 1 Hz and 3 Hz, and fH, between 12 Hz and 15 Hz.

For the EMG signals, the digital filter 43 preferably comprises at least a first bandpass filter of Butterworth type with programmable frequencies fL equal to 25 Hz and fH equal to 70 Hz and at least a second notch filter with programmable frequencies fL equal to 48 Hz and fH equal to 52 Hz.

For the EKG signal, the digital filter 44 preferably comprises a bandpass filter of Butterworth type with programmable frequencies fL and fH equal to 0.1 Hz and 40 Hz respectively.

The microcontroller 38 comprises a unit 50 for the storage management of the data, by means of which at least one polysomnographic signal EEG, EOG, EMG, EKG and the HRV signal detected by the photoplethysmography sensor 35 are stored within at least one memory unit 4. The device 1 may thus include an internal memory unit and/or an external memory unit (such as an SD card or USB Memory Stick). In particular, the device 1 may include a module 51 for receiving and transmitting data in order to transfer the acquired signals to an external display device (tablet, smartphone, PC, etc.)- The module 51 may further allow to change the options for the acquisition of the signals, or schedule the start and the end of the acquisition based on a specific time period.

Preferably, both the polysomnographic signals and the HRV signal are stored in ASCII and/or binary format, in order to directly view the acquired signals without the need for post-processing on the computer. The display may thus occur on any PC with a generic visualization software (e.g. MatLab, Octave, Excel, etc.) without the need for dedicated software.

The microcontroller 38 is further programmed to control the impedance detected from the electrodes in the assembly phase on the patient. In this way, it provides an immediate indication to the technician on the correctness of the assembly, allowing it to act rapidly with the addition of gel or paste in salt content to lower the contact impedance of the channel indicated by a red light.

The device 1 is similar to a Holter-type device, then wearable by the patient during sleep. In Figure 7 it is further indicated the management unit for the power supply with the reference 63. The device 1 can be powered directly from the mains or by batteries.

The values of polysomnographic signals and HRV are stored so as to be temporally synchronized with each other. In Figure 8 is shown an example of display of the signals acquired by means of the device according to the present invention. In the example of Figure 8 four graphs are shown, in which along the axis of abscissas is shown the time, while the values along the axis of ordinates represent the various quantities of interest; in particular, the graph 52 shows the HRV signal, the graphs 53, 54 and 55 respectively show the polysomnographic signals EOG, EMG (mylohyoid muscle) and EEG (Fz-Cz).

The method according to the present invention is based on experimental results of the inventors that have studied the circadian autonomic fluctuations of HRV values in a sample of PD patients with and without RBD symptoms. Clinical diagnosis of RBD, as well as the absence of RBD symptoms, have been confirmed by video polysomnographic registrations.

A median night (20.00-08.00)/median day (08.00-20.00) ratio (RBD index) of the frequency domain (LF, HF) in both groups of PD patients has been calculated. RBD index for LF spectral component, indicative of SNS functioning, and thus called sympathetic RBD index (S-RBD) differentiated at individual level, with an accuracy, sensitivity and specificity of 100%, PD-RBD patients from those without RBD. As shown in Figure 9, all PD-RBD patients had a S-RBD index >1.52 (range: 1.77-3.85) whereas all PD patients without RBD had a S-RBD <1.52 (range: 0.7-1.3 ). The cutoff value of 1.52 for the S-RBD index was calculated as the sum of the average of all the LF spectral component values and three times the standard deviation of the values.

No significant statistical differences were found for RBD index of HF (P-RBD index) component indicative of PSN functioning, as shown in Figure 10.

These results suggest that PD patients with S-RBD index > 1.52 had RBD confirmed by polysomnographic recordings.

Since RBD may clinically occur with very mild symptoms that need polysomnographic recordings for a definite diagnosis, the present invention propose the use of a simple and non-invasive measurement of sympathetic system, as a useful tool for identifying subjects suspected of having RBD and/or Lewy body disorders or other neurodegenerative diseases.

Figure 11 shows a flowchart with some steps of the method that are implemented by an embodiment of the portable device according to the present invention in order to calculate the S-RBD index for a subject on the basis of the values of RR intervals between two peaks of the heartbeat in the time domain. As previously disclosed, these values are obtained in the portable device from a signal detected by the photoplethysmography sensor 35.

RR values are filtered at step 100 through a smoothing and artifact removal filter, which throws away all values outside a certain range (e.g.: 0.5 - 1.5 s) and all the values that are more than x% different from the average of the n previous values, and reinterpolates values that are within a circular buffer 110. Typical values for x% are 10% or 20% and n between 10 and 20.

Filtered values are stored in the circular buffer of a number p of NN (normal-to- normal) values, with at least 300 values (p=300, i.e. about 5 minutes of RR events). The values stored in the buffer 110 are thus used to compute a Lomb-Scargle periodogram at step 120 in order to obtain corresponding values in the frequency domain. When a new value enters the circular buffer 110, the Lomb-Scargle periodogram is computed by the internal microcontroller 38.

The power of the computed periodogram is estimated at step 130 by calculating the integral of the periodogram in the frequency bands LF (0.04 - 0.15 Hz) and HF (0.15 - 0.4 Hz). LF/HF ratio is also evaluated.

A number k of LF and HF power spectral densities (and their ratios) are stored in a convenient number of buffers 140 and 150 in the internal/external memory 4 of the portable device 1. Average LF powers are then evaluated over night and day acquisition times, respectively, at step 160 and their ratio is computed and stored in the internal/external memory 4 of the portable device 1, or transmitted by the communication module 51 of the device 1 over a communication channel, e.g. a wireless communication channel, to an external receiving device such as a PC, a tablet, a smartphone or the like. All linear and non linear HRV parameters, in time- domain as well as in frequency domain, can be evaluated and analyzed by the microcontroller.

Figures 12 and 13 show another embodiment of the present invention, wherein EEG, EMG, EOG, EKG and HRV signals may be acquired locally by means of electrodes W1-W10 and PPG sensor Wl l integrating single-channel wireless amplifiers, capable of acquiring, conditioning and transmitting signals over a wireless communication channel (Bluetooth, Wi-Fi, ZigBee, etc..) to the device 1, located within a few meters from the patient, through the module 51 for receiving and transmitting data.

Signals may be stored in the on the local memory of the device (e.g. internal disk or removable SD/MMC card) in order to allow the processing of the same or may be transmitted to a PC over a serial/USB or wireless interface of the device 1. Various modifications may be made to the embodiments herein described by way of example without departing from the scope of the present invention. For example, in order to detect the EEG signals, additional electrodes can be provided for and positioned at different regions of the cerebral cortex, and are preferably arranged according to the international standard 10-20. Similarly, the acquisition of an electromyography signal EMG can also be done through a single pair of electrodes 21-22 or 23-24 or 25-26 positioned at one or more muscles of the patient.