Real-Time Epileptic Seizure Detection During Sleep Using Passive Infrared Sensors

Abstract

This paper addresses the problem of detecting epileptic seizures experienced by a human subject during sleep. Commonly used solutions to this problem mostly rely on detecting motion due to seizures using contact-based sensors or video-based sensors. We seek a low-cost, low-power alternative that can sense motion without making direct contact with the subject and provides high detection accuracy. We investigate the use of Passive InfraRed (PIR) sensors to sense human body motion caused by epileptic seizures during sleep which makes the body shake and causes the PIR sensor to generate an oscillatory output signal. This signal can be distinguished from that of ordinary motions during sleep using analysis with machine learning algorithms. The supervised hidden Markov model algorithm (HMM) and a 1-D and 2-D convolutional neural network (ConvNet) are used to classify the data set of the PIR sensor output into the occurrence of epileptic seizures, ordinary motions, or absence of motion. The method was tested on the PIR signals captured at 1 m from 33 recruited healthy subjects who, after watching seizure videos, either moved their body on a bed to simulate a seizure, ordinary motion, or lay still. The HMM algorithm attained 97.03% accuracy, while 1D-ConvNet and 2D-ConvNet attained an accuracy of 96.97% and 98.98%, respectively. All simulated seizures were successfully detected, with errors occurring only in distinguishing between ordinary motion and no motion, thereby demonstrating the potential for using PIR sensors in the epileptic seizure detection.Index Terms-PIR sensor, epileptic seizure, hidden Markov model (HMM), ConvNet, max-pooling, sensing unit (SU).

Computer vision for automated seizure detection and classification: A systematic review

“…also created an end‐to‐end model to analyze multiple frames, significantly improving the performance. Nocturnal BTC seizures have been detected in the infrared (IR) spectrum using thermophile sensor arrays and passive IR sensors to detect 1.6–3‐Hz periodic movements 13,51,57,60 . These devices, unlike IR illuminated cameras, produce unidentifiable images, protecting patient privacy while maintaining performance; models achieved up to 93.1% accuracy on patient data and accuracy of 99% on actor‐simulated seizures.…”

Applications of wireless sensor systems to sleep stage estimation for home sleep monitoring

“…In addition, Hanosh et al. [30] employed PIR sensors to sense human body motion caused by epileptic seizures during sleep. Also, for the detection of obstructive sleep apnoea, a wearable Sleepcare kit was developed in [31].…”

“…In [29], on the basis of strip-type force sensors, an unobtrusive sleep monitoring system was developed for awakening and respiration rate analysis during sleep. In addition, Hanosh et al [30] employed PIR sensors to sense human body motion caused by epileptic seizures during sleep. Also, for the detection of obstructive sleep apnoea, a wearable Sleepcare kit was developed in [31].…”

Online detection and removal of eye blink artifacts from electroencephalogram

“…Studies that are addressing real-time epileptic seizure detection techniques can be found in [145][146][147][148][149]. Hanosh et al [145] used Passive Infrared Sensors (PIR) for realtime epileptic seizure detection during sleep.…”

“…Studies that are addressing real-time epileptic seizure detection techniques can be found in [145][146][147][148][149]. Hanosh et al [145] used Passive Infrared Sensors (PIR) for realtime epileptic seizure detection during sleep. In this study, identifying and removing eyeblink artifacts are not applicable as the study focused on seizure detection during sleep, where no eye blinking activity occurs during this stage.…”