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Energy-Efficient and Robust Algorith...

Haghi, Benyamin,

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Energy-Efficient and Robust Algorithms for Biomedical Applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy-Efficient and Robust Algorithms for Biomedical Applications // Benyamin Haghi.
Author:	Haghi, Benyamin,
Description:	1 electronic resource (144 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Medical equipment. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31466320
ISBN:	9798384339724

Energy-Efficient and Robust Algorithms for Biomedical Applications /
Haghi, Benyamin,

Energy-Efficient and Robust Algorithms for Biomedical Applications /Benyamin Haghi. - 1 electronic resource (144 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Medical devices play a critical role in improving the quality of life for patients and assisting physicians by monitoring, detecting, and helping manage chronic conditions such as epilepsy and spinal cord injuries. To perform these functions effectively, these devices must extract the most relevant information from complex medical data. However, the functionality of these medical devices has been limited by the existing challenges in medical applications. Some of these challenges include the complexity in the analysis of raw medical data, adaptability, non-stationarity, noise, large data volumes, real-time processing, limited resources, and high accuracy demands. Moreover, considering factors such as individual differences, environmental influences, and genetic variations, medical data will cause numerous variations and uncertainties in analyzing and interpreting the medical conditions in different biomedical applications. Medical data analysis is already complex and is further complicated by issues like non-stationarity and noise, especially when using traditional and manual methods. When it comes to the designing, implementation, and utilization of wearable and implantable medical devices, efficiency, accuracy, and adaptability become crucial. Particularly, applications that require fast control of equipment, such as brain-machine interfaces (BMIs), make the need for fast decision-making evident. Medical data have been conventionally managed by reliance on extensive manual labor. However, such manual data management techniques are not scalable, have inefficient procedures, and are more likely to produce errors. Therefore, more advanced, automated methods are required immediately considering the existing challenges of the current medical data analysis techniques. Such a shift in data processing and management will lead to more trustable procedures that can significantly improve the accuracy and efficiency of medical data analysis. Other than being just an improvement, such transformation signifies a noteworthy point in the development of medical devices. In this view, it is essential to introduce advanced technology and novel methods for medical data processing as well as automation. Therefore, it becomes critical that these high-performance and advanced techniques can efficiently be implemented with minimum effects on hardware for clinical applications. Currently, artificial intelligence (AI) and its subfield machine learning (ML) has led to major transformations in designing and utilization of various medical devices. Among all these biomedical applications, three major area are addressed in this thesis: Brain Machine Interfaces (BMIs), seizure detection, and classification of arrhythmias in cardiac rhythms. We selected these three applications due to their significance and ability to improve patient treatment further. Additionally, we showed how we used machine learning algorithms for each of these applications to address their current challenges. In our work related to Brain-Machine Interfaces (BMIs), we have been focused on improving the quality of life for individuals with spinal cord injury (SCI) through two studies. In our initial study, we have designed and implemented a deep multi-state Dynamic Recurrent Neural Network (DRNN) decoder for BMI applications. This algorithm decodes neural data recorded from the posterior parietal cortex (PPC) and the motor cortex (M1) of human participants to appropriate control signals to predict computer cursor kinematics on the computer screen. By reducing the amount of history used in predicting the movement kinematics from the recorded neural data, we have demonstrated that improved performance and robustness are preserved while memory and power consumption are reduced. We then compared the performance of DRNN with other decoding techniques to demonstrate that when operating on wavelet-based neural features, our proposed DRNN-based decoder outperforms other decoding techniques.

English

ISBN: 9798384339724Subjects--Topical Terms:

3560831
Medical equipment.

Energy-Efficient and Robust Algorithms for Biomedical Applications /
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520 $a Medical devices play a critical role in improving the quality of life for patients and assisting physicians by monitoring, detecting, and helping manage chronic conditions such as epilepsy and spinal cord injuries. To perform these functions effectively, these devices must extract the most relevant information from complex medical data. However, the functionality of these medical devices has been limited by the existing challenges in medical applications. Some of these challenges include the complexity in the analysis of raw medical data, adaptability, non-stationarity, noise, large data volumes, real-time processing, limited resources, and high accuracy demands. Moreover, considering factors such as individual differences, environmental influences, and genetic variations, medical data will cause numerous variations and uncertainties in analyzing and interpreting the medical conditions in different biomedical applications. Medical data analysis is already complex and is further complicated by issues like non-stationarity and noise, especially when using traditional and manual methods. When it comes to the designing, implementation, and utilization of wearable and implantable medical devices, efficiency, accuracy, and adaptability become crucial. Particularly, applications that require fast control of equipment, such as brain-machine interfaces (BMIs), make the need for fast decision-making evident. Medical data have been conventionally managed by reliance on extensive manual labor. However, such manual data management techniques are not scalable, have inefficient procedures, and are more likely to produce errors. Therefore, more advanced, automated methods are required immediately considering the existing challenges of the current medical data analysis techniques. Such a shift in data processing and management will lead to more trustable procedures that can significantly improve the accuracy and efficiency of medical data analysis. Other than being just an improvement, such transformation signifies a noteworthy point in the development of medical devices. In this view, it is essential to introduce advanced technology and novel methods for medical data processing as well as automation. Therefore, it becomes critical that these high-performance and advanced techniques can efficiently be implemented with minimum effects on hardware for clinical applications. Currently, artificial intelligence (AI) and its subfield machine learning (ML) has led to major transformations in designing and utilization of various medical devices. Among all these biomedical applications, three major area are addressed in this thesis: Brain Machine Interfaces (BMIs), seizure detection, and classification of arrhythmias in cardiac rhythms. We selected these three applications due to their significance and ability to improve patient treatment further. Additionally, we showed how we used machine learning algorithms for each of these applications to address their current challenges. In our work related to Brain-Machine Interfaces (BMIs), we have been focused on improving the quality of life for individuals with spinal cord injury (SCI) through two studies. In our initial study, we have designed and implemented a deep multi-state Dynamic Recurrent Neural Network (DRNN) decoder for BMI applications. This algorithm decodes neural data recorded from the posterior parietal cortex (PPC) and the motor cortex (M1) of human participants to appropriate control signals to predict computer cursor kinematics on the computer screen. By reducing the amount of history used in predicting the movement kinematics from the recorded neural data, we have demonstrated that improved performance and robustness are preserved while memory and power consumption are reduced. We then compared the performance of DRNN with other decoding techniques to demonstrate that when operating on wavelet-based neural features, our proposed DRNN-based decoder outperforms other decoding techniques.
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