Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

Complex & Intelligent Systems https://doi.org/10.1007/s40747-021-00627-z ORIGINAL ARTICLE Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features Kuldeep Singh1 · Jyoteesh Malhotra2 Received: 17 April 2021 / Accepted: 17 December 2021 © The Author(s) 2022 Abstract Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated seizure prediction techniques could assist the patients, their family, and medical personnel to control and manage these seizures. This paper proposes a spectral feature-based two-layer LSTM network model for automatic prediction of epileptic seizures using long-term multichannel EEG signals. This model makes use of spectral power and mean spectrum amplitude features of delta, theta, alpha, beta, and gamma bands of 23-channel EEG spectrum for this task. Initially, the proposed single-layer and two-layer LSTM models have been evaluated for EEG segments having durations in the range of 5–50 s for 24 epileptic subjects, out of which EEG segments of 30 s duration are found to be useful for accurate seizure prediction using two-layer LSTM model. Afterwards, to validate the performance of this classifier, the spectral features of 30 s duration EEG segments are fed to random forest, decision tree, k-nearest neighbour, support vector machine, and naive Bayes classifiers, which are empowered with grid search-based parameter estimation. Finally, the iterative simulation results and comparison with recently published existing techniques firmly reveal that the proposed two-layer LSTM model with EEG spectral features is an effective technique for accurately predicting seizures in real time with an average classification accuracy of 98.14%, average sensitivity of 98.51%, and average specificity of 97.78%, thereby enabling the epileptic patients to have a better quality of life. Keywords Deep learning · Epilepsy · EEG · Healthcare · LSTM · Seizure prediction Introduction Epilepsy is a commonly occurring chronic nervous disorder characterized by the occurrence of spontaneous and sudden seizures [1]. This neurological disorder affects the lives of all age groups from infants to old age persons, covering approximately 50 million people around the world [2]. This count is getting worse in developing countries like India. As per statistical figures of Indian Epilepsy Centre, New Delhi, approximately 10 million Indian population is suffering from this disorder and this number is increasing B Kuldeep Singh Jyoteesh Malhotra 1 Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab 143005, India 2 Department of Engineering and Technology, Guru Nanak Dev University Regional Campus, Jalandhar, Punjab 144007, India day-by-day with the annual addition of 0.5–1 million new epileptic patients [3]. This fatal disorder may result in vital medical symptoms like abnormal behaviour, muscle cramps, strange sensations, and loss of consciousness, etc., which could lead to major injuries, brain damage or deaths of its patients in road accidents or during working in hazardous work environments [1]. Despite occurring at low frequency, these uncontrolled seizures critically influence the normal quality of life of epileptic patients. In general, more than 99.95% times, epileptic patients are not suffering from any seizure and should be entitled to live normal life, which could also reduce the socioeconomic burden on patients and their families [4]. This idea could be achieved by predicting epileptic seizures well before their actual onset. It would help in saving the lives of patients by sending timely alerts, thereby enabling them to take precautionary measures. Epileptic seizures cause a rapid upsurge in electrical disturbances in the patient’s brain, which can be measured using the electroencephalogram (EEG) technique [5]. Usually, EEG signal recordings are examined by neurologists 123 Complex & Intelligent Systems to determine different stages of epilepsy like ictal (on-going seizures), preictal (just before seizure onset), post-ictal (after seizure onset period), and interictal (in-between seizures) [6]. However, this process is arduous and time-consuming, which leads to the need for an automatic epileptic seizure prediction system [7,8]. Nowadays, Internet of things (IoT) technologies have started playing a key role in providing solutions to various health-related problems with the help of machine learning algorithms being deployed at cloud-based servers. These healthcare solutions may include elderly care [9], remote healthcare [10], fitness programs [11], detection and prognosis of neurological and mental disorders like Alzheimer, epilepsy, autism spectrum disorder and schizophrenia, etc. [12–17]. Deep learning [18] is another paradigm in this regard, which is capable of handling the large volume of signal data generated by wearable IoT sensing devices like EEG headsets for epilepsy [19]. The algorithms based on deep learning techniques overcome the limitations of traditional machine learning algorithms by offering less processing time and capability of handling big data of multichannel biomedical signals [20]. Consequently, these techniques play a promising role in providing real-time solutions in healthcare sector. To provide a smart solution to the patients with epilepsy, the present paper proposes a spectral feature-based two-layer long short-term memory (LSTM) model [21] for automated prediction of epileptic seizures. This approach makes use of long-term CHB-MIT [22] EEG database of 24 cases of epilepsy, collected at Children’s Hospital, Boston. First of all, the raw 23 channel EEG signals being recorded from the patient’s scalp are pre-processed, filtered, and segmented into short-duration segments in the range of 5–50 s. Then, these segments are converted into frequency domain using Fast Fourier transform (FFT), and are separated into five frequency bands for accurate interpretation of functional and behavioural features of a complex structure of the brain during epileptic seizures [23]. The given frequency bands of 23 channel EEG signals are characterized by extracting two distinct features—spectral power and mean spectrum amplitude. Initially, this work proposes single-layer LSTM (1L-LSTM) and two-layer LSTM (2L-LSTM) models for seizure prediction, which utilizes spectral features of various sub-bands of given EEG segments. This analysis of 1L-LSTM and 2LLSTM for different duration values of EEG segments reveals the effectiveness of 30 s duration EEG segments for seizure prediction using 2L-LSTM. Furthermore, to ensure the effectiveness of the proposed 2L-LSTM model, its performance has been compared with that of decision tree, random forest, k-nearest neighbour (kNN), support vector machine (SVM), naive Bayes, and 1L-LSTM classifiers for EEG segments of 30 s. These traditional mach (...truncated)