For patients with epilepsy, automatic epilepsy monitoring, i.e., the process of direct observation of the patient’s health status in real time, is crucial. Wearable systems provide the possibility of real-time epilepsy monitoring and alerting caregivers upon the occurrence of a seizure. In the context of the ICASSP 2023 Seizure Detection Challenge, we pro- pose a lightweight machine-learning framework for real-time epilepsy monitoring on wearable devices. We evaluate our proposed framework on the SeizeIT2 dataset from the wear- able SensorDot (SD) of Byteflies. The experimental results show that our proposed framework achieves a sensitivity of 73.6% and a specificity of 96.7% in seizure detection

Abtahi Fahliani, Azra

Aminifar, Amir

Huang, Baichuan

English

Lund University Publications

LUND UNIVERSITYPO Box 117221 00 Lund+46 46-222 00 00Lightweight Machine Learning for Seizure Detection on Wearable DevicesHuang, Baichuan; Abtahi Fahliani, Azra; Aminifar, AmirPublished in:ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 20232023Document Version:Publisher's PDF, also known as Version of recordLink to publicationCitation for published version (APA):Huang, B., Abtahi Fahliani, A., & Aminifar, A. (2023). Lightweight Machine Learning for Seizure Detection onWearable Devices. In ICASSP, the International Conference on Acoustics, Speech, and Signal Processing 2023IEEE - Institute of Electrical and Electronics Engineers Inc..Total number of authors:3General rightsUnless other specific re-use rights are stated the following general rights apply:Copyright and moral rights for the publications made accessible in the public portal are retained by the authorsand/or other copyright owners and it is a condition of accessing publications that users recognise and abide by thelegal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private studyor research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portalRead more about Creative commons licenses: https://creativecommons.org/licenses/Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will removeaccess to the work immediately and investigate your claim.LIGHTWEIGHT MACHINE LEARNING FOR SEIZURE DETECTIONON WEARABLE DEVICESBaichuan Huang, Azra Abtahi, Amir AminifarDepartment of Electrical and Information Technology, Lund University, Sweden{baichuan.huang, azra.abtahi fahliani, amir.aminifar}@eit.lth.seABSTRACTFor patients with epilepsy, automatic epilepsy monitoring,i.e., the process of direct observation of the patient’s healthstatus in real time, is crucial. Wearable systems providethe possibility of real-time epilepsy monitoring and alertingcaregivers upon the occurrence of a seizure. In the contextof the ICASSP 2023 Seizure Detection Challenge, we pro-pose a lightweight machine-learning framework for real-timeepilepsy monitoring on wearable devices. We evaluate ourproposed framework on the SeizeIT2 dataset from the wear-able SensorDot (SD) of Byteflies. The experimental resultsshow that our proposed framework achieves a sensitivity of73.6% and a specificity of 96.7% in seizure detection.Index Terms— Seizure Detection, Lightweight Machinelearning, Wearable IoT.1. INTRODUCTIONEpilepsy is one of the most common neurological disordersthat affects around 65 million people worldwide. Despite theadvances in anti-epileptic drugs, one-third of epileptic pa-tients still suffer from recurrent seizures with a high relapserate. Furthermore, people with epilepsy (PWE) have a 2–3times higher mortality rate compared to the correspondinghealthy population, mainly because of seizure-triggered acci-dents and Sudden Unexpected Death in Epilepsy (SUDEP).Video-electroencephalogram (EEG) recording is the goldstandard of epilepsy monitoring. However, the standard fullscalp-EEG recording setup has several limitations for patientmonitoring outside the hospital environment. In contrast,automated EEG-based seizure detection on wearable devicesprovides the possibility of real-time patient monitoring inambulatory settings.Smart wearable techniques can detect the onset of seizuresin real time and alert family members and caregivers for res-cue. However, wearable devices have stringent resourceconstraints, including limited memory storage, computingThis research has been partially supported by the Wallenberg AI, Au-tonomous Systems and Software Program (WASP), the Swedish ResearchCouncil (VR), the ELLIIT Strategic Research Environment, and the RESoRTSwiss Botnar Foundation project.power, and battery lifetime. Hence, lightweight machine-learning models tailored to wearable devices are indispens-able for the realization of real-time epilepsy monitoring. Inthe context of the ICASSP 2023 Seizure Detection Chal-lenge, we propose a lightweight machine-learning frameworkfor automated seizure detection on wearable devices. Ourexperimental evaluation on the dataset from the wearableSensorDot (SD) of Byteflies demonstrates that our frame-work achieves a sensitivity of 73.6% and a specificity of96.7% in seizure detection on the validation data.2. LIGHTWEIGHT MACHINE LEARNING2.1. Lightweight Seizure DetectionAs discussed before, in this paper, we consider epilepsy mon-itoring using resource-constrained wearable technologies.Here, we propose a lightweight seizure detection frameworkfor such wearable systems. Our proposed framework is basedon the Random Forest (RF) algorithm and power features incertain frequency bands of the EEG data.Power features are extracted from several frequencybands, namely [0, 0.1] Hz, [0.1, 0.5] Hz, delta [0.5, 4] Hz,theta [4, 8] Hz, alpha [8, 12] Hz, [12, 13] Hz, beta [13, 30] Hz,and gamma [30, 45] Hz [1]. The time complexity of comput-ing the power features can be reduced to the theoretical lowerbound of O(n), where n is the length of the EEG data [2].Towards this, we first filter the signal in the desired frequencyband using a time-domain band-pass filter. Next, we use Par-seval’s theorem on the filtered signal to obtain the power ofthe signal in the desired frequency band.Once the power features are calculated, we use the RF al-gorithm to obtain the final prediction outcome. The RF algo-rithm uses an ensemble of decision trees to make predictions.Each individual decision tree in the forest is relatively shal-low compared to a single deep decision tree and only needsto perform a limited number of comparisons during the infer-ence process. Finally, the final prediction of the RF algorithmis obtained by simply voting on the prediction outcomes of alldecision trees in the forest, which is a lightweight process interms of computational complexity for real-time inference onwearable devices.1:1 1:2 1:3 1:4 1:5 1:6ratio0.40.50.60.70.80.91.0Sensitivity0.40.50.60.70.80.91.0Specificity1:1 1:2 1:3 1:4 1:5 1:6ratio0100200300400500600700FA/hFig. 1. Sensitivity, Specificity, FA/h for different values ofratio between seizure and non-seizure.2.2. Data-Centric Seizure DetectionIn epilepsy monitoring, Sensitivity captures the performanceof the machine-learning algorithms in detecting positive sam-ples, i.e., seizures. On the other hand, the false-alarm ratecaptures the number of incorrectly detected seizures over acertain time interval. Excessive false-alarm rates often lead tounnecessary disruptions, inconvenience, and costs in seizuredetection. Therefore, False Alarm (FA) Rate or FA/h is highlyrelevant in the context of seizure detection. Figure 1 illus-trates the relevant evaluation metrics based on the ratio ofseizure and non-seizure data in the training set. If ratio in-creases from 1 : 1 to 1 : 6, Specificity gradually increasesto 1.0 and FA/h shows a rapid decline. Although Sensitivitydoes not manifest an apparent decreasing trend, high values ofratio lead to a significant reduction in Sensitivity. In conclu-sion, the value of ratio should be carefully selected to makean optimal trade-off between FA/h and Sensitivity.Here, we utilize the ChronoNet architecture [3] and fo-cus on the data manipulation techniques. The hyperparam-eter monitor is to save the best Keras model by monitoringthe assigned metric and the hyperparameter class weights isfor weighted average loss between seizure and non-seizure.We optimize the values of ratio, monitor, and class weightsto strike a balance between Sensitivity and FA/h.3. EXPERIMENTAL RESULTSIn this section, we consider two datasets: SeizeIT1 [4] andSeizeIT2 [5]. The SeizeIT1 dataset is acquired by the goldstandard video-EEG system. The SeizeIT2 dataset is acquiredby the wearable SensorDot (SD) of Byteflies. We evaluate ourproposed framework on Validation Data, i.e., Patients 25, 45,and 69 in SeizeIT2.3.1. Lightweight Seizure DetectionWe use the Validation Data with leave-one-patient-out cross-validation to train the RF model with the power features.We consider ratio = 1 : 3 following the insights illus-trated in Figure 1. Table 1 shows the performance of ourproposed framework for lightweight seizure detection. Forevaluation on Validation Data, the average value of the leave-one-patient-out cross-validation is calculated. The cross-validation is conducted three times. Our lightweight frame-work achieves a Sensitivity of 73.6% and a Specificity of96.7% on average. For the final RF model, we use all theValidation Data to train a RF model with the power features.Table 1. Performance of Lightweight Seizure DetectionEvaluation Sensitivity(%) Specificity(%)Validation Data 73.6 96.73.2. Data-Centric Seizure DetectionWe use SeizeIT1 data to train the ChronoNet with monitor =val spec, class weights = 1 : 1, and ratio = 1 : 3. Ta-ble 2 presents the performance of data-centric seizure detec-tion. For evaluation on Validation Data in SeizeIT2, we traina ChronoNet model using the 85% of patients in SeizeIT1 (theremaining 15% is for monitor). This leads to a Sensitivity of15.2% and a Specificity of 99.8% on the Validation Data.Table 2. Performance of Data-Centric ChronoNetEvaluation Sensitivity(%) Specificity(%)Validation Data 15.2 99.84. REFERENCES[1] Sopic D, Aminifar A, and Atienza D, “e-Glass: A wear-able system for real-time detection of epileptic seizures,”in IEEE International Symposium on Circuits and Sys-tems (ISCAS), 2018, pp. 1–5.[2] Surrel G, Aminifar A, Rincón F, et al., “Online obstruc-tive sleep apnea detection on medical wearable sensors,”IEEE Transactions on Biomedical Circuits and Systems(TBioCAS), vol. 12, no. 4, pp. 762–773, 2018.[3] Roy S, Kiral-Kornek I, and Harrer S, “Chrononet: a deeprecurrent neural network for abnormal eeg identification,”in AIME 2019, Poznan, Poland, June 26–29, 2019, Pro-ceedings 17. Springer, 2019, pp. 47–56.[4] Chatzichristos C and Claro Bhagubai M, “SeizeIT1,”2023.[5] Chatzichristos C, Swinen L, Macea J, et al., “Multimodaldetection of typical absence seizures in home environ-ment with wearable electrodes,” Frontiers in Signal Pro-cessing, p. 66, 2022.

Lightweight Machine Learning for Seizure Detection on Wearable Devices

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Lightweight Machine Learning for Seizure Detection on Wearable Devices

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