A wearable monitoring system for nocturnal epileptic seizures

Ungureanu, Constantin; Bui, Vinh; Roosmalen, Wouter; Aarts, Ronald M.; Arends, Johan; Verhoeven, Richard; Lukkien, Johan J.

doi:10.1109/ismict.2014.6825205

Cited by 12 publications

(15 citation statements)

References 18 publications

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“…Recently, Ungureanu et al [16] has proposed the use of a different sensor modality for detection of nocturnal epileptic seizures, due to ambiguity on the placement of the accelerometer on the patient and to identify seizures that do not normally manifest as motor seizures. However, for unobtrusive and ambulatory monitoring of patients the challenge is to have a device and a method with minimum number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Gubbi

Kusmakar

Rao

et al. 2016

IEEE J. Biomed. Health Inform.

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Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Gubbi

Kusmakar

Rao

et al. 2016

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Most ECG-based seizure detection systems from the literature are based on patient-independent models (10)(11)(12)(13). For this type of models, no patient-specific data is required, making them directly usable in practice.…”

Section: Introductionmentioning

confidence: 99%

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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“…However a wearable solution is preferred. In [ 14 , 15 ] wearable ECG devices are used for seizure detection. These studies focused on nocturnal epileptic seizures only, and in only a few patients, three and five respectively.…”

Section: Introductionmentioning

confidence: 99%

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

Vandecasteele

Cooman

et al. 2017

Sensors

155

120

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Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.

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A wearable monitoring system for nocturnal epileptic seizures

Cited by 12 publications

References 18 publications

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

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

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

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