CROSS REFERENCE TO RELATED APPLICATIONS

[001] The present application claims priority to an Indian provisional application, 3035/MUM/2014, filed on September 23, 2014, entirety of which is enclosed for reference.

TECHNICAL FIELD

[002] The present disclosure generally relates to health care devices. More specifically, the present disclosure relates to a system and a method for assessing health care devices based on a quality index.

BACKGROUND

[003] Health technology is one of most essential support of health care systems in today's world. Health care devices (HCDs) are crucial in prevention, diagnosis, and treatment of illness and diseases, as well as patient rehabilitation. Typically, the HCDs may be used either alone or in combination with any accessory, consumable, or any other health care equipment. The HCDs require calibration, maintenance, repair, user training, and decommissioning regularly. The HCDs are usually managed by clinical engineers or technicians associated with hospitals or manufacturers of the HCDs.

[004] World Health Organization (WHO) has taken cognizance in prioritizing and deploying the HCDs with quality. In order to provide HCDs with quality, there is a need to assess the HCDs for potential impacts on performance of the HCDs and on users/patients.

[005] Generally, the assessment of HCDs occurs while updating an inventory related to the HCDs, services related to the HCDs and/or when replacing associated services. The assessment may be performed under varying circumstances. Further, the assessment may be conducted based on the manuals, specifications and literatures provided with the HCDs by the manufacturers. Such kind of assessment may be termed as a deterministic assessment. However, the deterministic assessment excludes circumstances that affect the day to day functioning and performance of the HCDs. Therefore, the deterministic assessments are not holistic in nature. SUMMARY

[006] This summary is provided to introduce concepts related to systems and methods for assessing a plurality of Health Care Devices (HCDs) and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.

[007] In one implementation, a method for assessing a plurality of Health Care Devices (HCDs) is disclosed. The method comprises extracting cognitive load features of plurality of patients using at least two Health Care Devices (HCDs). The cognitive load features are extracted using Electroencephalography (EEG) signals captured by the at least two HCDs. The method further comprises calculating, by a processor, a performance index of the at least two HCDs based on the cognitive load features extracted. The performance index is calculated by performing an anova analysis on the cognitive load features extracted. The method further comprises determining a significance factor to calculate the performance index based on the anova analysis. The method further comprises calculating, by the processor, a usability index of the at least two HCDs based on the cognitive load features extracted. The usability index is calculated based on an ergonomic quality and a difficulty level of each of the at least two HCDs. The ergonomic quality represents a feedback of a patient in extracting the cognitive load features. The difficulty level indicates a setup time taken for each of the at least two HCDs. The method further comprises normalizing, by the processor, the performance index and the usability index calculated for each of the at least two HCDs. The method further comprises calculating an average of the performance index and the usability index normalized. The method further comprises determining, by the processor, a quality index of the at least two HCDs based on the performance index and the usability index normalized to assess the at least two HCDs.

[008] In one implementation, a system for assessing a plurality of Health Care Devices (HCDs) is disclosed. The system comprises a memory and a processor coupled to the memory. The processor executes program instructions stored in the memory to extract cognitive load features of plurality of patients using at least two Health Care Devices (HCDs). The cognitive load features are extracted using Electroencephalography (EEG) signals captured by the at least two HCDs. The processor further executes the program instructions stored in the memory to calculate a performance index of the at least two HCDs based on the cognitive load features extracted. The performance index is calculated by performing an anova analysis on the cognitive load features extracted. The processor further executes the program instructions stored in the memory to determine a significance factor to calculate the performance index based on the anova analysis. The processor further executes the program instructions stored in the memory to calculate a usability index of the at least two HCDs based on the cognitive load features extracted. The usability index is calculated based on an ergonomic quality and a difficulty level of each of the at least two HCDs. The ergonomic quality represents a feedback of a patient in extracting the cognitive load features. The difficulty level indicates a setup time taken for each of the at least two HCDs. The processor further executes the program instructions stored in the memory to normalize the performance index and the usability index calculated for each of the at least two HCDs. The processor further executes the program instructions stored in the memory to calculate an average of the performance index and the usability index normalized. The processor further executes the program instructions stored in the memory to determine a quality index of the at least two HCDs based on the performance index and the usability index normalized to assess the at least two HCDs.

[009] In one implementation, a non-transitory computer readable medium embodying a program executable in a computing device for assessing a plurality of Health Care Devices (HCDs) is disclosed. The program comprises a program code for extracting cognitive load features of plurality of patients using at least two Health Care Devices (HCDs). The cognitive load features are extracted using Electroencephalography (EEG) signals captured by the at least two HCDs. The program further comprises a program code for calculating a performance index of the at least two HCDs based on the cognitive load features extracted. The performance index is calculated by performing an anova analysis on the cognitive load features extracted. The program further comprises a program code for calculating a usability index of the at least two HCDs based on the cognitive load features extracted. The usability index is calculated based on an ergonomic quality and a difficulty level of each of the at least two HCDs. The ergonomic quality represents a feedback of a patient in extracting the cognitive load features. The difficulty level indicates a setup time taken for each of the at least two HCDs. The program further comprises a program code for normalizing the performance index and the usability index calculated for each of the at least two HCDs. The program further comprises a program code for determining a quality index of the at least two HCDs based on the performance index and the usability index normalized to assess the at least two HCDs. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like/similar features and components.

[0011] FIG. 1 illustrates a network implementation of a system for assessing a plurality of Health Care Devices (HCDs), in accordance with an embodiment of the present disclosure.

[0012] FIG. 2 illustrates the system, in accordance with an embodiment of the present disclosure.

[0013] FIG. 3 shows a method for extracting cognitive load features from a patient using HCDs, in accordance with an embodiment of the present disclosure.

[0014] FIG. 4 shows a flowchart for assessing a plurality of Health Care Devices (HCDs), in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0015] The present disclosure relates to a system and a method for assessing a plurality of Health Care Devices (HCDs). At first, cognitive load features of plurality of patients may be extracted using at least two Health Care Devices (HCDs). The cognitive load features may be extracted using Electroencephalography (EEG) signals captured by the at least two HCDs. The at least two devices may have similar configurations. After extracting the cognitive load features, a performance index of the at least two HCDs may be calculated. The performance index may be calculated by performing an anova analysis on the cognitive load features extracted. Further, a significance factor may be determined to calculate the performance index. Subsequently, a usability index of the at least two HCDs may be calculated based on the cognitive load features extracted. Specifically, the usability index may be calculated based on an ergonomic quality and a difficulty level of each of the at least two HCDs. The ergonomic quality may represent a feedback of a patient in extracting the cognitive load features. The difficulty level may indicate a setup time taken for each of the at least two HCDs.

[0016] Subsequently, the performance index and the usability index may be normalized. After normalizing the performance index and the usability index, an average of the performance index upon normalization and the average of the usability index upon normalization may be calculated. Further, a quality index of the at least two HCDs may be determined based on the average of the performance index normalized and average of the usability index normalized to assess the at least two HCDs.

[0017] While aspects of described system and method for assessing a plurality of Health Care Devices (HCDs) may be implemented in any number of different computing systems, environments, and/or configurations, the embodiments are described in the context of the following exemplary system.

[0018] Referring now to FIG. 1, a network implementation 100 of a system 102 for assessing a plurality of Health Care Devices (HCDs) is illustrated, in accordance with an embodiment of the present disclosure. The system 102 may extract cognitive load features of plurality of patients using at least two Health Care Devices (HCDs). The system 102 may calculate a performance index of the at least two HCDs based on the cognitive load features extracted. The system 102 may calculate a usability index of the at least two HCDs based on the cognitive load features extracted. The system 102 may normalize the performance index and the usability index calculated for each of the at least two HCDs. Subsequently, the system 102 may calculate an average of the performance index and the usability index normalized. The system 102 may determine a quality index of the at least two HCDs based on the performance index and the usability index normalized to assess the at least two HCDs.

[0019] Although the present disclosure is explained by considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, cloud, and the like. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2... 104-N, collectively referred to as user devices 104 hereinafter, or applications residing on the user devices 104. Examples of the user devices 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user devices 104 are communicatively coupled to the system 102 through a network 106.

[0020] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[0021] Referring now to FIG. 2, the system 102 is illustrated in accordance with an embodiment of the present disclosure. In one embodiment, the system 102 may include at least one processor 202, an input/output (I O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the at least one processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 206.

[0022] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with a user directly or through the user devices 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.

[0023] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0024] In one implementation, at first, the user may use the client device 104 to access the system 102 via the I/O interface 204. The working of the system 102 may be explained in detail using FIG. 2 and FIG. 3. The system 102 may be used to assess a plurality of Health Care Devices (HCDs). Specifically, the system 102 may assess the plurality of HCDs using a Quality index (QI) of each of the HCDs. The QI may indicate a relevance of the HCD for detecting cognitive load of the patient. Generally, the QI may be expressed in a probabilistic manner. The QI of each HCDs may be expressed by comparing the HCDs having same specification. The HCDs provided by different manufacturers having same specification, manuals may be compared to check if HCD is relevant in a given context or not. Result of the comparison may provide a deterministic assessment to choose one HCD from a plurality of HCDs. However, considering different demography and a different environment, effectiveness of HCD may vary. The variation in the effectiveness may be probabilistic in nature. Further, certain HCD assessments may affect measurements depending upon an Intensive Care Unit (ICU) design space, characteristics of a technician in handling/setting up the health care devices. Furthermore, the HCD assessment may depend on a patent's characteristics in responding to health care devices including patient's feedback and other factors that are related to a particular context and usability of the health care device. Therefore, it is important to assess the HCDs present in a hospital based on QI.

[0025] In order to assess the HCDs in the hospital, at least two HCDs having similar specification may be selected. After selecting the at least two HCDs, the QI may be determined for each of the at least two HCDs. After determining the QI, user of the HCDs may come to a conclusion in deciding which of the two HCDs is optimal for use in a given scenario.

[0026] Referring to the FIG. 3, a plurality of HCDs, 302-1, 302-2, 302-3,... 302-n (Collectively referred as 302) used to capture Electroencephalography (EEG) signals of cognitive load features of the patient 304, is shown. The HCDs 302 may be connected to the system 102 for determining the QI of each of the HCDs 302. The HCDs 302 may be connected to the system 102 either wirelessly or through wired connection. The system 102 may be enabled to receive signals of data transmitted by the HCDs 302. The data transmitted may depend upon the kind of HCDs 302 implanted.

[0027] Furthermore, the signal may be associated with characteristics of health of the patient 304. In one embodiment, if the technician observes any abnormal reading over the HCDs 302, the technician may trigger the HCDs 302 to transmit the data signal towards the system 102. Before initiating a medical test/treatment on the patient 304, the technician may input ON time of treatment/ test. Similarly, OFF time may also be fed to the system 102. After transmitting the signal, the system 102 may receive data from the user/the patient/technicians 304 in form of feedback upon completion of the test/the treatment. The feedback may be taken either offline or online.

[0028] Furthermore, the system 102 may be triggered automatically or may be enabled manually by the technician to receive the data through the signal transmitted by the HCDs 302. In one aspect, the system 102 may be fed with a reference normal condition data. The reference normal condition data may comprise data associated with the patient 304 during normal operative conditions i.e., the signal indicative of normal condition of the patient 304. The normal condition of the patient 304 may further reveal the signal quality transmitted by the HCDs 302 associated with the patient 304. In another aspect, the reference normal condition data may be fed once in a life time. Further, the reference normal condition data may vary from one HCD to other HCD. In another aspect, the reference normal condition data along with the abnormal data associated with the patient 304 may be used for the computation of the performance metrics related to the HCDs 302.

[0029] In one implementation, the HCDs 302 may be used to extract cognitive load features of the patient 304. Specifically, the HCDs may extract the cognitive load features using Electroencephalography (EEG) signals captured by the HCDs 302. The EEG signals are captured as modality of analyzing brain signal is attractive as HCDs used to capture noninvasive in nature, portable and offers optimal time resolution. Moreover, the brain signals provide a more direct way of measuring the cognitive load as cognition takes place in human brain. Other modalities of different physiological signals such as pupil dilation, heart rate variability, and galvanic skin response may provide indirect way of measuring cognitive load. The EEG signals may be preferred and are widely used for reliable measurement of the cognitive load. For assessing the HCDs, low cost HCDs may be preferred as they easily wear out, uses less data volume and requires less computational power.

[0030] Although the HCDs with low cost is preferred for assessment, the low cost HCDs have less number of channels, lower Analog to Digital Converter (ADC) resolution and lower sampling frequency. The HCDs 302 comprising the lower sampling frequency (typically 128 Hz or 256 Hz) may not pose difficulty as the brain signal are mostly limited to 30Hz for BCI applications. Having less number of channels may pose problems as vital information may be missed out if exact brain lobes are not probed. For example, visual perception related information may be processed in occipital lobe whereas cognitive information i.e. complex thinking, decision making etc. are mostly processed in the frontal lobe. Therefore, if certain EEG channels are not available, the information may be lost. Further, the low cost EEG devices offer lower spatial resolution for reduced number of channels when compared to the other similar EEG devices.

[0031] For selecting an optimal HCD, at least two HCDs 302 may be chosen for comparison. For example, two low cost EEG devices, Emotiv ^® and Neurosky ^® may be selected. After selecting the at least two HCDs 302, each HCD 302 may be implanted on the patient 304 to measure cognitive load based on memory related tasks. As known, Emotiv ^® may have 14 channels (AF3, AF4, F3, F4, F7, F8, FC5, FC6, P3, P4, P7, P8, T7, T8, 01, and 02) and the Neurosky ^® may have a single channel (Fpl). It is to be understood that the current example is restricted to memory based stimuli and two class classification problem i.e. classifying a high or low cognitive load. Further, the example is used to probe right and left frontal lobes with the Emotiv® to get more granular information. More specifically the Emotiv ^® right frontal lobe (AF4, F4, F8, and FC5), the Emotiv ^® left frontal lobe (AF3, F3, F7, FC6) and the Neurosky ^® prefrontal lobe (Fpl) signal are considered. Each of the HCDs 302 are used to capture performance metrics and based on the performance metrics; an optimal HCD to detect the cognitive load may be selected.

[0032] For measuring the cognitive load of the patients 304, the experiment setup comprises 10 patients, 9 male and 1 female, in the age group of 30 to 35. All the patients are right-handed software professionals. The experiment further comprises of 10 trials in a session and there may be a 5 second relaxing slide between two consecutive trials in a session. The patients are allowed to move and stretch in the relaxation period that appears in between any consecutive trials. Each session either has all low or all high cognitive load stimulus and there is no mixing of the low or the high cognitive load in one session. Each patient may take two sessions for the low cognitive load task and two sessions for the high cognitive load task in an alternate manner. All the patients are given 10 minutes resting time in between the sessions. Experiments associated with the low load may be performed before experiments associated with the high load. After each session all the patients may be asked to rate task levels using NASA-TLX. As known, the NASA-TLX is a subjective workload assessment tool. The NASA-TLX enables to perform subjective workload assessments on patients 304 working with various human-machine systems. Specifically, the NASA-TLX is a multi-dimensional rating procedure that derives an overall workload score based on a weighted average of ratings on six subscales. Subsequently, the data is post processed with MATLAB for analysis and result generation. As the patients are put into a controlled environment during the experiment, all the patients maintains minimal movement (only finger movement for mouse button press) and visible artifact may be introduced due to eye movements of the patients. Therefore, preprocessing stage focuses on correcting the eye blink artifacts. As known, the Neurosky ^® and Emotiv ^® devices detect the eye movements. The resulting EEG signal is then fed to a feature extraction algorithm. The feature extraction may be further explained in detail in the following description.

[0033] During the experiment all are the patients were shown stimuli and asked to respond if a particular criterion is met. The particular criterion may differentiate the low cognitive load task from the high cognitive load task. The window of 2.5 second was cut around the time of response which served as a trial epoch i.e., total length was 5 seconds for the trial epoch. After each task, i.e., low or high cognitive load tasks, the patients were allowed to take rest for 5 seconds which served as a baseline epoch. After each trial, epochs and the baseline epochs were S-transformed to decompose the non-stationary EEG signal in time-frequency domain for better precision. In the next step, an alpha band (7.5 to 12.5 Hz) and a theta band (4 to 7.5 Hz) mean frequency and power at mean frequencies for all the trials, i.e., epoch and baseline and baselines are calculated separately for all 'N' EEG leads i.e. data from 'N' channels. The mean frequency for the epoch is calculated using an expression as follows.

ω is frequency band in question, n is the number of frequency bins in the co, / is the frequency at bin i and / is the energy density of the ω at frequency bin i.

[0034] Subsequently, the average mean frequency shift between the trial and the baseline epochs ^Δ Ι*' ^: 'I and ^ ^'l are calculated for alpha (a) and theta (Θ) respectively. Further, the total cognitive load L (t) for the trial t is calculated using an equation as follows.

L it) = Δ f a ) (« ) - Δ /, (0 ¾/, (ø )

[0035] In first configuration, right four frontal channels of the Emotive® are used (N=4). In subsequent configuration, left four frontal channels are used (N=4), and in last configuration single channel Neurosky® is used (N=l).

[0036] In the next step, the computation of the QI for both the Emotive® and the Neurosky® may be performed. According to the exemplary embodiment, a few metrics may be derived to reflect quantitative measures of quality used for the purpose for comparison between the HCDs i.e., the Emotive® and the Neurosky®. The Quality Index (QI) may be computed based on average of Performance Index (PI) and average of Usability Index (UI) as described below.

[0037] After extracting the cognitive load features, based on the configuration, the performance index (PI) of each of the HCDs 302 may be calculated. Specifically, the PI may be calculated by performing an anova analysis on the cognitive load features extracted. The PI may be calculated using an equation as follows.

PI = F _ value x SFactor

F_ value is standard ANOVA based measure. SFactor is a significance factor, i.e., either 0 or 1 depending on the ANOVA based P value being greater than 0.05 or less than 0.05 respectively. The ANOVA analysis may be used to measurement quality of the HCDs 302. A higher F_ value and lower P value may indicate the discriminative power of the feature derived from the signals received from the HCDs, and hence, the HCDs ability to detect the high cognitive loads and the low cognitive loads if fed to a classifier. For the cognitive load features extracted, the F_value obtained for each configuration for the 10 patients is shown in Table 1.

[0038] Table 1: F_value

Table 1

[0039] Further, the P_value and the significance factor, SFactor obtained may be presented in Table 2. P value less than 0.001 is shown as 0 in the Table 2. [0040] Table 2: P value and SFactor

Table 2

[0041] After obtaining the F_value and the SFactor, the performance index (PI) may be calculated using the expression, PI= F_value X SFactor. Subsequently, the PI may be normalized (P_ normalized) using an equation as follows.

PI - PI _ sin

P _ normalized = ^■

PI _ - PI _ urn

PI_ max is the maximum and PI_ min is the minimum values among all the three configurations i.e., the Emotiv® right, left and the Neurosky® single lead measured features. In order to illustrate calculation of PI and P_ normalized, Table 3 may be used as an example. Specifically, Table 3 shows the PI and P_normalized calculated for above example. [0042] Table 3: Calculation of PI and P_normalized

Table 3

[0043] After calculating the P-normalized, an average of the PI may be calculated. In one example, the average of PI i.e., PI _ avg may be calculated using an equation as follows.

PI _ avg =— PI _ normalized n is the number of measurements in the particular configuration. For the above example, n=10 as feature value was measured for 10 patients for each configuration.

[0044] Referring to Table 3, it may be observed that left frontal 4 channel, the Emotiv® data gives a better result to detect cognitive load task based on memory operations as the average of PI_ value normalized is highest i.e., 0.33. In addition, the configuration may have highest number of the Significance Factor (SFactor) to be valid, i.e., SFactor =1, when all the patients may be evaluated. The Neurosky® measurement falls short as the average normalized PI is reported to be lowest i.e. 0.05. Since the prefrontal lobe is farthest from the skull surface and the signals associated with the cognitive load might have been decayed if tried to pick up from the forehead only.

[0045] In one implementation, the HCDs 302 may be assessed from a usability point of view, where ease of use in terms of ergonomics and difficulty for setting up experiments for functioning of the HCDs 302 are considered. For example, time taken for setting up the experiment may be considered. In another example, time taken to pair the system 102 and the HCDs 302 using Bluetooth connectivity may be considered. Generally, the Emotiv® takes more time for setting up due to use of wet sensors takes time to obtain good connectivity for all the channels and/ or establishing wireless connectivity. In order to overcome the issue of time, headset position may be adjusted repeatedly. Further, more conductive liquid may put as a trial and error process. However, the trial and error process may take longer time. Based on ease of using the HCDs 302 and the time taken to setup the HCDs 302, a usability index (UI) may be calculated.

[0046] For calculating the UI, Ergonomic Quality (EQ) and a Difficulty Level (DL) of each of the HCDs 302 may be calculated. The EQ may represent a feedback of the patient 304 corresponding to use of the HCDs 302 in extracting the cognitive load features. The DL may indicate the setup time taken for each of the HCDs 302. Both, the EQ and the DL may be presented in a range of 0 to 5. Where 0 indicates unsatisfactory and 5 indicate complete satisfactory. For the above experiment, scaling of the DL for the setup time may be shown using Table 4.

[0047] Table 4: DL scaling based on setup time

Table 4

[0048] The time taken to setup and the feedback received from the patients 304 for the above experiment may be shown using Table 5. [0049] Table 5: EQ and DL

Table 5

[0050] After obtaining the EQ and the DL, the Usability Index (UI) may be calculated for each HCD 302. The UI may be calculated using an equation as follows.

UI = EQ+DL

[0051] After the UI is calculated, the UI may be normalized. The UI may be normalized using an equation as follows.

IM — ZM _ ni_a

UI _ normalized = ^:

UI _ max — UI _ n o

[0052] In order to illustrate calculation of UI and the UI normalized, Table 6 may be presented. Table 6: UI and UI normalized

Table 6

[0053] Subsequently, an average of the normalized UI may be calculated. The average of the UI normalized may be calculated using an equation as follows.

1

UI _ m>g =— 13 _ nom lized

n _n

[0054] For the above example, the average of the UI normalized is shown in Table 6. After calculating the average of PI normalized and the average of the UI normalized, the QI of the HCDs 302 may be calculated. The quality index (QI) may be computed using an equation as follows.

QI=PI_avg X UI_avg

[0055] Referring to Table 3 and Table 6, the QI of the HCDs 302 may be determined. The QI determined for the above experiment may be presented in the Table 7. Table 7: Determining QI

Table 7

[0056] Referring to Table 7, overall performance of the HCDs 302, i.e. the Emotiv® and the Neurosky® using three configurations on basis of the QI is shown. It may be evident from the Table 7 that the left frontal lobe four channels in the Emotiv® provides the most efficient configuration. Further, the Neurosky® is easier to use as the Neurosky® has only one channel. Therefore, the technician may select left frontal lobe four channels in the Emotiv® for measuring cognitive load.

[0057] Since the F_Value and P value may give overall measurement quality, the QI may capture many aspect of probabilistic natured characteristics associated with the HCDs and overall surrounding environment that are mainly coming from human factors. The QI captures the user/technicians 304 friendliness and the experiment setup difficulties. If too much drop in the QI is observed, preventive steps may be taken by altering the position/location of the HCDs, altering the HCDs itself, or altering the user/technician 304 accordingly.

[0058] Referring now to FIG. 4, a method 400 for assessing a plurality of Health Care Devices (HCDs) is shown, in accordance with an embodiment of the present disclosure. The method 400 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method 400 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.

[0059] The order in which the method 400 is described and is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400 or alternate methods. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the disclosure described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 400 may be implemented in the above- described system 102.

[0060] At step/block 402, cognitive load features of plurality of patients may be extracted using at least two Health Care Devices (HCDs). The cognitive load features are extracted using Electroencephalography (EEG) signals captured by the at least two HCDs.

[0061] At step/block 404, a performance index of the at least two HCDs may be calculated based on the cognitive load features extracted. The performance index may be calculated by performing an anova analysis on the cognitive load features extracted.

[0062] At step/block 406, a usability index of the at least two HCDs may be calculated based on the cognitive load features extracted. The usability index may be calculated based on an ergonomic quality and a difficulty level of each of the at least two HCDs. The ergonomic quality may represent a feedback of a patient in extracting the cognitive load features. The difficulty level may indicate a setup time taken for each of the at least two HCDs.

[0063] At step/block 408, the performance index and the usability index calculated may be normalized for each of the at least two HCDs.

[0064] At step/block 410, a quality index of the at least two HCDs may be determined based on the performance index and the usability index normalized to assess the at least two HCDs.

[0065] Although implementations for method(s) and system(s) for assessment of health care devices based on quality index have been described in language specific to structural features and/or methods, it is to be understood that the implementations and/or embodiments are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for assessment of health care devices.