Waqar Waqar 2024a - Revision history

Tomamil: Tomamil moved page Review 208317528385 to Waqar Waqar 2024a

2024-09-28T12:36:28Z

Tomamil moved page Review 208317528385 to Waqar Waqar 2024a

Ayaan waqar at 14:24, 17 August 2024

2024-08-17T14:24:28Z

Ayaan waqar at 14:19, 17 August 2024

2024-08-17T14:19:15Z

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Ayaan waqar at 05:47, 16 August 2024

2024-08-16T05:47:35Z

Ayaan waqar at 05:47, 16 August 2024

2024-08-16T05:47:11Z

Ayaan waqar: All changes asked by the reviewer

2024-08-15T06:36:03Z

All changes asked by the reviewer

Ayaan waqar at 06:34, 15 August 2024

2024-08-15T06:34:05Z

Ayaan waqar at 11:08, 13 August 2024

2024-08-13T11:08:21Z

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Ayaan waqar at 10:58, 12 August 2024

2024-08-12T10:58:48Z

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Ayaan waqar at 10:09, 12 August 2024

2024-08-12T10:09:49Z

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 '''Image 3:''' Prototype Picture Appearance and Worn<span id="_GoBack"></span>
-Federated Machine Learning (FML) represents a paradigm shift in the processing and analysis of data for the Epilet Band, a device designed for real-time detection of Convulsive Epileptic Seizures (Babar et al., 2024). Unlike traditional machine learning methods that require data to be centralized, FML leverages a distributed approach to train models directly on the device, ensuring personal data never leaves the user's environment (Chen et al., 2024). This methodology significantly enhances user privacy by minimizing the risk of data breaches and ensuring sensitive health information is processed locally. Additionally, FML facilitates the continuous improvement of the Epilet Band's seizure detection algorithms through collective learning, where only model updates, and not the raw data, are shared across devices. Therefore, it is worth nothing that the sensitivity and specificity of the results is directly related to number of users, period of data collected and the algorithm’s learning iterations. This approach not only preserves privacy but also allows for a scalable system that improves over time with each user's data contributing to the refinement of the model without compromising individual data security (Yang et al., 2019).
+FML represents a paradigm shift in the processing and analysis of data for the Epilet Band, a device designed for real-time detection of Convulsive Epileptic Seizures (Babar et al., 2024). Unlike traditional machine learning methods that require data to be centralized, FML leverages a distributed approach to train models directly on the device, ensuring personal data never leaves the user's environment (Chen et al., 2024). This methodology significantly enhances user privacy by minimizing the risk of data breaches and ensuring sensitive health information is processed locally. Additionally, FML facilitates the continuous improvement of the Epilet Band's seizure detection algorithms through collective learning, where only model updates, and not the raw data, are shared across devices. Therefore, it is worth nothing that the sensitivity and specificity of the results is directly related to number of users, period of data collected and the algorithm’s learning iterations. This approach not only preserves privacy but also allows for a scalable system that improves over time with each user's data contributing to the refinement of the model without compromising individual data security (Yang et al., 2019).
 The data transfer protocol introduced with the Epilet Band plays a pivotal role in its ability to ensure user privacy and data security. At the heart of this protocol is the ATmega328P microcontroller, which is ingeniously repurposed as a local server within the device. This microcontroller allows for real-time processing with minimal latency, ensuring that the detection of a seizure can trigger an immediate alert to caregivers or medical personnel. This rapid response capability is crucial for the timely intervention that could be lifesaving in the event of a severe seizure (Krieger & Litt, 2009). The protocol's design thus not only respects and protects user privacy but also ensures the Epilet Band can provide a highly responsive and effective seizure detection service.
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 The identification and statistical relevance of what is termed the "perfect impulse" is pivotal in refining the seizure detection capabilities of the Epilet Band. This impulse pattern represents the archetype of sensor outputs during convulsive epileptic seizures (CES), distinctly differentiated from patterns generated by normal daily activities. The process of identifying this pattern was meticulous, leveraging an extensive dataset obtained from a modelling and simulating seizure movements and engaging in routine activities, all while fed into the Epilet Band.
-''Data Collection and Analysis'': The initial phase involved the collection of diverse data sets, encompassing both simulated seizures and a range of everyday motions. This was critical in establishing a broad foundation upon which the Federated Machine Learning (FML) algorithm could learn and discern. The analysis phase utilized the FML approach to meticulously sift through the sensor data, isolating unique patterns that accurately corresponded to seizure activities. This critical step ensured the elimination of irrelevant data, focusing solely on patterns indicative of seizures.
+''Data Collection and Analysis'': The initial phase involved the collection of diverse data sets, encompassing both simulated seizures and a range of everyday motions. This was critical in establishing a broad foundation upon which the FML algorithm could learn and discern. The analysis phase utilized the FML approach to meticulously sift through the sensor data, isolating unique patterns that accurately corresponded to seizure activities. This critical step ensured the elimination of irrelevant data, focusing solely on patterns indicative of seizures.
 ''Optimization Process'': Through iterative training and validation, the algorithm was refined to enhance its sensitivity (true positive rate) and specificity (true negative rate), thus improving its overall accuracy. This optimization was essential in minimizing both false positives and false negatives, ensuring the model’s efficacy in accurately detecting seizures.
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 For years, common approaches to detect and control epileptic seizures and refractory epilepsy, including a gamma knife, second-line drugs, gene therapy, and pharmacogenetics, have been popular (Holmes & Noebels, 2016; Srinivas, 2010). However, these methods have faced considerable backlash due to their ineffectiveness. Second-line drugs have been ineffective in catering to reducing epileptic seizures. At the same time, the success of non-invasive and invasive procedures is still under question when there are no surgically remediable lesions to report. Additionally, there are also the long term side effects attached to the improper treatment of epilepsy, which can dissuade people from opting for those treatments.
-Therefore, this study puts forth the notion of the Epilet band, which is designed to gauge the perfect impulse of an individual by monitoring their day-to-day activities. The band addresses generalized tonic-clonic seizures (GTCS) as it is the most prevalent seizure type in South Asia and tracks physical changes for timely detection. The Epilet Band is trained through the Edge Impulse Studio and is connected to a mobile application developed using Android Studio to display alerts received by the watch. The Epilet band works through an ATmega328P microcontroller. This microcontroller acts as the intermediary between the sensor data collected by the Epilet Band and the Federated Machine Learning (FML) model that processes this data. When the device detects potential seizure activity, it processes this information locally, using the FML model stored within the microcontroller. This local processing means that sensitive data does not need to be sent to a central server for analysis, significantly reducing the risk of intercepting or misusing personal health information. The only data that ever leaves the device are the encrypted updates to the machine learning model based on the aggregated insights from the device's analyses. These updates are then shared with a central model to improve its accuracy for all users, but they contain no personally identifiable information or raw data from the sensors.
+Therefore, this study puts forth the notion of the Epilet band, which is designed to gauge the perfect impulse of an individual by monitoring their day-to-day activities. The band addresses generalized tonic-clonic seizures (GTCS) as it is the most prevalent seizure type in South Asia and tracks physical changes for timely detection. The Epilet Band is trained through the Edge Impulse Studio and is connected to a mobile application developed using Android Studio to display alerts received by the watch. The Epilet band works through an ATmega328P microcontroller. This microcontroller acts as the intermediary between the sensor data collected by the Epilet Band and the FML model that processes this data. When the device detects potential seizure activity, it processes this information locally, using the FML model stored within the microcontroller. This local processing means that sensitive data does not need to be sent to a central server for analysis, significantly reducing the risk of intercepting or misusing personal health information. The only data that ever leaves the device are the encrypted updates to the machine learning model based on the aggregated insights from the device's analyses. These updates are then shared with a central model to improve its accuracy for all users, but they contain no personally identifiable information or raw data from the sensors.
 Identifying epileptic seizures immediately is crucial to save an individual from a life-threatening emergency. Although there have been no clinical trials with the watch yet, as it is still in its preliminary stages, the experimental results showcase its efficacy in seamlessly detecting seizures. While epilepsy belts are an emerging development, initial results showcase several challenges to their success. For instance, belts are heavy to wear and comply with, which may put many people at an unease with their usage altogether. Moreover, a blueprint for data privacy and ownership is lacking, as they do not follow FML guidelines yet.

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 The ability to detect severe seizures is crucial for immediate medical intervention. This figure underscores the importance of the device's capacity to recognize and react to life-threatening seizures, highlighting the study's aim to offer a comprehensive seizure management solution.
-=4. Conclusion and Future Work:=
+=4. Conclusion:=
 <span id='_Hlk164849705'></span>The paper presents a theoretical model integrating artificial intelligence into healthcare practices. The research findings demonstrate the efficacy of the proposed Epilet band in detecting uncontrollable convulsive seizures timely.  The machine learning model will allow for increased accuracy as the number of trials and sample size are increased. The fast reaction time will help in detecting seizures and sending out alerts to reduce fatalities or unfortunate accidents caused by a sudden onset of a seizure.

@@ Line 96: / Line 96: @@
 Furthermore, this microcontroller allows for real-time processing with minimal latency, ensuring that the detection of a seizure can trigger an immediate alert to caregivers or medical personnel. This rapid response capability is crucial for the timely intervention that could be lifesaving in the event of a severe seizure (Krieger & Litt, 2009). The protocol's design thus not only respects and protects user privacy but also ensures the Epilet Band can provide a highly responsive and effective seizure detection service.
-=3. Results and discussion:=
+=3. Discussion and results:=
 <span id='_Hlk164849507'></span>The Epilet Band works by differentiating between epileptic seizures and day-to-day activities, like brushing one’s teeth, and it achieves this by detecting the muscle moving pattern produced by the two actions as shown in Figure 2, Figure 3, Figure 4, and Figure 5.

@@ Line 58: / Line 58: @@
 '''Figure 1: '''Epilet Band conceptual design
-[[File:1..png|left|thumb]]
+[[File:1..png|thumb|none]]
 '''Image 1:''' Epilet Band schematic design
@@ Line 80: / Line 80: @@
 To expand, a mobile app was developed using Android Studio, specifically for the Arduino Nano BLE Communication to add features such as the emergency call feature, axis tracking feature, and data storage. This is especially helpful for users trying to track their medical record. Furthermore, the front end of the app is optimized for minimal distracts with a simple interface. The app also presents the history of the alerts to show any recognisable pattern in alerts, while also displaying any active messages for the caregiver and user.
-The prototype image is presented in Image 2 and Image 3 to show the size of the band and its appearance. Image 4 presents the prototype placed on a seizure generator.
+The prototype image is presented in Image 2 and Image 3 to show the size of the band and its appearance.
-[[File:WhatsApp Image 2024-08-15 at 11.14.15 AM.jpg|left|thumb|200x200px]]
+[[File:WhatsApp Image 2024-08-15 at 11.14.15 AM.jpg|thumb|200x200px|none]]
 '''Image 2:''' Prototype Picture Placed on Seizure Generator
-[[File:WhatsApp Image 2024-08-15 at 11.14.14 AM.jpg|left|thumb|201x201px]]
+[[File:WhatsApp Image 2024-08-15 at 11.14.14 AM.jpg|thumb|201x201px|none]]
 '''Image 3:''' Prototype Picture Appearance and Worn<span id="_GoBack"></span>

@@ Line 58: / Line 58: @@
 '''Figure 1: '''Epilet Band conceptual design
 [[File:1..png|left|thumb]]
 '''Image 1:''' Epilet Band schematic design
@@ Line 81: / Line 81: @@
 The prototype image is presented in Image 2 and Image 3 to show the size of the band and its appearance. Image 4 presents the prototype placed on a seizure generator.
-[[Image:Draft_Waqar_717482369-image3.jpeg|180px]]
+'''Image 2:''' Prototype Picture Placed on Seizure Generator
-'''Image 2:''' Prototype Picture Appearance and Worn
+'''Image 3:''' Prototype Picture Appearance and Worn<span id="_GoBack"></span>
-−
-[[Image:Draft_Waqar_717482369-image4.jpeg|156px]]
-−
-'''Image 3:''' Prototype Picture Placed on Seizure Generator<span id="_GoBack"></span>
-−
-[[Image:Draft_Waqar_717482369-image5.jpeg|210px]]
-−
-'''Image 4:''' Federated Machine Learning and Data Transfer Protocol
 Federated Machine Learning (FML) represents a paradigm shift in the processing and analysis of data for the Epilet Band, a device designed for real-time detection of Convulsive Epileptic Seizures (Babar et al., 2024). Unlike traditional machine learning methods that require data to be centralized, FML leverages a distributed approach to train models directly on the device, ensuring personal data never leaves the user's environment (Chen et al., 2024). This methodology significantly enhances user privacy by minimizing the risk of data breaches and ensuring sensitive health information is processed locally. Additionally, FML facilitates the continuous improvement of the Epilet Band's seizure detection algorithms through collective learning, where only model updates, and not the raw data, are shared across devices. Therefore, it is worth nothing that the sensitivity and specificity of the results is directly related to number of users, period of data collected and the algorithm’s learning iterations. This approach not only preserves privacy but also allows for a scalable system that improves over time with each user's data contributing to the refinement of the model without compromising individual data security (Yang et al., 2019).

← Older revision	Revision as of 12:36, 28 September 2024
(No difference)

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 Furthermore, this microcontroller allows for real-time processing with minimal latency, ensuring that the detection of a seizure can trigger an immediate alert to caregivers or medical personnel. This rapid response capability is crucial for the timely intervention that could be lifesaving in the event of a severe seizure [18]. The protocol's design thus not only respects and protects user privacy but also ensures the Epilet Band can provide a highly responsive and effective seizure detection service.
-=3. Discussion and results analysis:=
+=3. Results and discussion:=
 <span id='_Hlk164849507'></span>The Epilet Band works by differentiating between epileptic seizures and day-to-day activities, like brushing one’s teeth, and it achieves this by detecting the muscle moving pattern produced by the two actions as shown in Figure 2, Figure 3, Figure 4, and Figure 5.