Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

Vandecasteele, Kaat; Cooman, Thomas De; Gu, Ye; Cleeren, Evy; Claes, Kasper; Paesschen, Wim Van; Huffel, Sabine Van; Hunyadi, Borbála

doi:10.3390/s17102338

Cited by 149 publications

(126 citation statements)

References 23 publications

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“…Currently, a large portion of false detections and a small percentage of missed seizures is caused due to poor ECG quality, largely due to the wiring. A previous study has shown that better performance can be obtained by using a wearable ECG device rather than the standard wired hospital ECG (27). This would however be the case for all evaluated algorithms in this study, including those from the literature tested in this study on the discussed dataset.…”

Section: Limitations Of the Studymentioning

confidence: 80%

“…Personalizing the algorithm will not help to detect these seizures without ictal heart rate changes. Also false detections or missed seizures caused by too strong ECG noise cannot be avoided by the personalization as they can occur both ictally and inter-ictally (27). Other approaches should be used to further improve the performance, such as improved noise removal techniques.…”

Section: Performance Comparison Of the Pi And Tl Approachmentioning

confidence: 99%

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Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

et al. 2020

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Objective: Automated seizure detection is a key aspect of wearable seizure warning systems. As a result, the quality of life of refractory epilepsy patients could be improved. Most state-of-the-art algorithms for heart rate-based seizure detection use a so-called patient-independent approach, which do not take into account patient-specific data during algorithm training. Although such systems are easy to use in practice, they lead to many false detections as the ictal heart rate changes are patient-dependent. In practice, only a limited amount of accurately annotated patient data is typically available, which makes it difficult to create fully patient-specific algorithms. Methods: In this context, this study proposes for the first time a new transfer learning approach that allows to personalize heart rate-based seizure detection by using only a couple of days of data per patient. The algorithm was evaluated on 2,172 h of single-lead ECG data from 24 temporal lobe epilepsy patients including 227 focal impaired awareness seizures. Results: The proposed personalized approach resulted in an overall sensitivity of 71% with 1.9 false detections per hour. This is an average decrease in false detection rate of 37% compared to the reference patient-independent algorithm using only a limited amount of personal seizure data. The proposed transfer learning approach adapts faster and more robustly to patient-specific characteristics than other alternatives for personalization in the literature. Conclusion: The proposed method allows an easy implementable solution to personalize heart rate-based seizure detection, which can improve the quality of life of refractory epilepsy patients when used as part of a multimodal seizure detection system.

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Section: Limitations Of the Studymentioning

confidence: 80%

Section: Performance Comparison Of the Pi And Tl Approachmentioning

confidence: 99%

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

et al. 2020

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“…Behind‐the‐ear EEG is not the only type of signal suitable for a wearable seizure detection system in an outpatient setting. Electrocardiography (ECG), which can be recorded in an outpatient setting, has added value to automatically detect seizures in temporal lobe epilepsy patients . Previous studies investigated multimodal seizure detection algorithms .…”

Section: Discussionmentioning

confidence: 99%

Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels

et al. 2020

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Objective: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting.We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. Methods: Fifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model). Results:The visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patientindependent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. Significance: Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours.

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“…Therefore, only generalized onset seizure can be detected. [8] This work compared the performance of wrist-worn PPG device and wearable ECG. The result was stated that the performance was good by using wearable EEG.…”

Section: Literature Review On Iot Based Seizure Detection Work Inmentioning

confidence: 99%

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Nanthini*,

Kavitha,

Sivabalaselvamani

et al. 2019

IJRTE

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— Unpredictable nature of epilepsy, patients only has more need of awareness about precautions and how to handle the occurrence. Epilepsy belongs to have a recurrent seizures tendency throughout the life. Seizure may happen due to number of reasons like tumor, head injury, pregnancy time, genetic etc. It can be curable with proper diagnosis, incurable but controllable with lifelong medication and remaining are uncontrollable that leads to death. Recording of alert symptoms like auras, prodromes and precipitant factors are helped to self-alert the patient, create positive impact on quality of life and increase the efficacy of treatments. The need of enhancing early seizure detection and developing wearable monitoring product with low cost is used to create fear free environment among the affected people. In this connection, my proposed work reviewed on existing and currently available IOT based seizure detection and alert systems feasibility.

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Automated Epileptic Seizure Detection Based on Wearable ECG and PPG in a Hospital Environment

Cited by 149 publications

References 23 publications

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

Visual seizure annotation and automated seizure detection using behind‐the‐ear electroencephalographic channels

Epilepsy Seizure Detection and Prediction Based on DeviceHive

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