Nocturnal Frontal Lobe Epilepsy Research Articles

Classifier Combination Supported by the Sleep-Wake Cycle Improves EEG Seizure Prediction Performance.

Seizure prediction is a promising solution to improve the quality of life for drug-resistant patients, which concerns nearly 30% of patients with epilepsy. The present study aimed to ascertain the impact of incorporating sleep-wake information in seizure prediction. We developed five patient-specific prediction approaches that use vigilance state information differently: i) using it as an input feature, ii) building a pool of two classifiers, each with different weights to sleep/wake training samples, iii) building a pool of two classifiers, each with only sleep/wake samples, iv) changing the alarm-threshold concerning each sleep/wake state, and v) adjusting the alarm-threshold after a sleep-wake transition. We compared these approaches with a control method that did not integrate sleep-wake information. Our models were tested with data (43 seizures and 482 hours) acquired during presurgical monitoring of 17 patients from the EPILEPSIAE database. As EPILEPSIAE does not contain vigilance state annotations, we developed a sleep-wake classifier using 33 patients diagnosed with nocturnal frontal lobe epilepsy from the CAP Sleep database. Although different patients may require different strategies, our best approach, the pool of weighted predictors, obtained 65% of patients performing above chance level with a surrogate analysis (against 41% in the control method). The inclusion of vigilance state information improves seizure prediction. Higher results and testing with long-term recordings from daily-life conditions are necessary to ensure clinical acceptance. As automated sleep-wake detection is possible, it would be feasible to incorporate these algorithms into future devices for seizure prediction.

Nocturnal paroxysmal dystonia to sleep-related hypermotor epilepsy: A critical review.

Sleep-related paroxysmal motor episodes (SPMEs) have been described by various names, including nocturnal paroxysmal dystonia, nocturnal frontal lobe epilepsy (NFLE), and sleep-related hypermotor epilepsy. The underlying pathophysiology has been debated over the years, with these episodes assumed to be a form of paroxysmal dystonia or parasomnia versus a form of epilepsy. In most studies published on SPMEs and their variants (paroxysmal arousals, nocturnal paroxysmal dystonia, and episodic nocturnal wanderings) in the early 1990s, the authors speculated on the pathophysiology but did not commit to one idea. It was not until the mid-1990s that epilepsy became the leading prospect. We performed a narrative review of the major articles that have described this syndrome in a chronological fashion. We identified three eras, 1972-1993, 1994-1998, and 1999 to the present, each era marked by a landmark study. Our critical review of these early studies shows that the neurophysiological data supporting epilepsy as the sole basis for all SPME cases is very weak. In 1994-1995, a familial pattern of this syndrome was described and the term autosomal dominant NFLE was coined, with the authors claiming that all their patients had a form of frontal lobe epilepsy. With the exception of a few reference cases, the neurophysiological evidence that all patients had frontal lobe epilepsy was very weak. Compared to articles published on surgical series of frontal lobe epilepsy, the percentage of SPME cases with positive interictal/ictal electroencephalograms remained very low, seriously questioning the epileptic basis of the syndrome. Our critical review and analysis of the published literature shows that the evidence presented in favor of SPMEs being a homogenous focal epilepsy syndrome is very weak. Neurologists must recognize that SPMEs could be a form of movement disorder, parasomnia, or epilepsy. We recommend a pragmatic semiology-based classification of these episodes using the four-dimensional classification system.

An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Investigation of Heartbeat Evoked Potential (HEP) Response During Different Stages of Sleep in Sleep Disorders

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

CABP4 mutation in mice shows alteration in protein expression level and neuron discharge frequency.

The calcium-binding protein 4 (CABP4) gene is a newly identified epilepsy-related gene that might be associated with a rare type of genetic focal epilepsy; that is, autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE). In vitro, mutant CABP4 causes an increased inward flow voltage of calcium ions and a significant increase in the electrical signal discharge in hippocampus neurons; however, the role of CABP4 in epilepsy has not yet been specifically described, and there is not yet a CABP4 mutant animal model recapitulating the epilepsy phenotype. We introduced a human CABP4 missense mutation into the C57BL/6J mouse genome and generated a knock-in strain carrying a glycine-to-aspartic acid mutation in the gene. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were performed to evaluate the CABP4 expression level. Slice patch-clamp recording was carried out on pyramidal cells of prefrontal cortex layers II and III. The CABP4G155D/+ mutant mice were viable and born at an expected Mendelian ratio. Surprisingly, the heterozygous (HE) mice did not display either an abnormal appearance or an overt seizure phenotype, and there was no statistically significant difference between the HE and wild-type (WT) mice in terms of overall messenger RNA (mRNA) and protein expression. However, the HE mutant mice showed an imbalance in the amount of protein expressed in the brain regions. Additionally, the patch-clamp recordings from the HE mouse layer II/III cortical pyramidal cells revealed an increase in the frequency of micro-excitatory post-synaptic currents (mEPSCs) but no change in the amplitude was observed. The findings of this study suggest that the CABP4 p.G155D mutation might be one of the mechanisms underlying seizure onset.

Malignant Infantile Osteopetrosis: A Rare Cause of Refractory Hypocalcemia

Background: Infantile malignant osteopetrosis (IMO) is a rare autosomal recessive disease characterized by a higher bone density in bone marrow caused by the dysfunction of bone resorption. Clinical Description: A 2-month-old baby boy presented with features of lower respiratory tract infection, along with a swollen right upper arm due to fracture of humerus, along with signs of bicytopenia and hepatosplenomegaly. He had a history of hypocalcemic seizures at 8th day of life. Management and Outcome: Current hospitalization revealed refractory hypocalcemia and hypophosphatemia with normal to increased Vitamin D and parathormone levels and increased density of bone. Hypocalcemia was particularly refractory in nature needing multiple intravenous calcium corrections. Whole exome sequencing detected compound heterozygous variants in the T-cell immune regulator 1 pathogenic gene of IMO. Simultaneously, a heterozygous nonsense variation in exon 4 of the CHRNA4 gene was detected causing nocturnal frontal lobe epilepsy. As the infant had evidence of bone marrow failure, he was considered and referred for hematopoietic stem cell transplantation. Conclusion: Neonatal and early infantile hypocalcemia are commonly seen by pediatricians, but an etiology of IMO is rarely considered. The presence of fracture of long bones with cytopenias and hepatosplenomegaly in a young infant with recurrent hypocalcemia, without Vitamin D deficiency, should raise suspicion of this disorder and genetic analysis should be carried out for the same.

The Association of Nocturnal Seizures and Interictal Cardiac/Central Autonomic Function in Frontal Lobe Epilepsy: Heart Rate Variability and Central Autonomic Network Analysis.

Patients with epilepsy frequently experience autonomic dysfunction, closely related to sudden unexplained death in epilepsy (SUDEP). SUDEP occurs most often at night or during sleep, and frequent nocturnal seizures are an established risk factor. This study investigated the influence of nocturnal seizures on autonomic dysfunction in epilepsy. This retrospective study enrolled frontal lobe epilepsy (FLE) patients who performed 24-hour EEG monitoring. All participants were divided into nocturnal FLE (NFLE, > 90% of seizures occurring during sleep) or diurnal FLE (DFLE) groups. EEG and ECG signals were simultaneously obtained during waking and sleep stages. EEG current density source and connectivity analysis of the autonomic network were performed. ECG was analyzed across time and frequency domains heart rate variability (HRV) analysis method was used. The obtained parameters were compared between the NFLE and DFLE groups. Fifteen NFLE and 16 DFLE patients were enrolled with no significant difference in age, sex, disease duration, seizure frequency, or the number of anti-seizure medications between the two groups. During sleep, a decrease in HRV parameters and an increase of the beta-1 (13-22 Hz) current source density power in the bilateral paracentral lobule (BA4,5,6), precuneus (BA7), and cingulate (BA31) were observed in the NFLE group compared to DFLE group. The NFLE group also showed hyperconnectivity in the central autonomic (12 edges distributed over 10 nodes), sympathetic (2 edges distributed over 3 nodes), and parasympathetic (4 edges distributed over 6 nodes) beta-1 frequency band networks during sleep. During wakefulness, central and cardiac autonomic variables were not significantly different between the NFLE and DFLE groups. Interictal cardiac and central autonomic dysfunction occurred simultaneously and can be attributed to the brain-heart autonomic axis. Our findings suggest that nocturnal seizures may contribute to interictal autonomic dysfunction during sleep in people with epilepsy.

CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals

The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP).Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.

Two novel variants of the STXBP1 and CHRNB2 genes identified in a Chinese boy with refractory seizures and developmental delay.

Autosomal dominant sleep-related hypermotor epilepsy is a rare disease caused by pathogenic variants of CHRNB2, CHRNA4, and CHRNA2 genes, with nocturnal frontal lobe epilepsy as the main symptoms. Syntaxin binding protein 1 (STXBP1) gene mutation can cause developmental and epileptic encephalopathy 4, mainly presenting as a developmental and epileptic encephalopathy. We performed the exome-targeted next-generation sequencing in our patient and identified two heterozygous variants: c.963 + 2T>C of STXBP1 and c.520_527delinsTGCTAC (p.R174Cfs*16) of CHRNB2. Molecular analysis was performed of the variant c.963 + 2T>C. Aberrantly spliced products were observed, proving the pathogenicity of this variant. Refractory seizures and developmental delay could be explained. Although the variant c.520_527delinsTGCTAC could cause the truncation of the proteins, it was ultimately determined to be nonpathogenic. The startle-like responses that occurred occasionally during the night were ultimately determined to be an uncommon phenotype caused by the STXBP1 variant.

The Atlas of Monogenic Epilepsies

Epilepsy may be the consequence of several causes, including genetic anomalies, structural brain malformations, hypoxic-ischemic encephalopathy, brain tumors, and drugs, all contributing to the imbalance between excitatory and inhibitory neurons and modulatory interneurons, which in turn provoke abnormal, simultaneous electric discharge(s) involving part or all the brain.[1] [2] [3] In the pregenetic, pregenomic era, in most cases, the exact cause of such neuronal/interneuronal disequilibrium remained unknown and the term idiopathic epilepsy was used to define all the epilepsies without cause. At the same time, some specific epileptic syndromes were indicated by the eponym of the first physician who originally described the condition (e.g., West syndrome, Dravet syndrome, Ohtahara syndrome, Lennox–Gastaut syndrome) or by some characteristic clinical features (e.g., nocturnal frontal lobe epilepsy, absence epilepsy, epilepsy and mental retardation limited to females). In many of these occurrences, the distinct epileptic syndrome was defined mainly by its most relevant clinical feature (e.g., seizure semiology), associated comorbidities, and electroencephalographic patterns.[3] [4]

Difference of Brain Electrical Activity Mappings in Sleep Stages

According to a survey by the World Health Organization, the proportion of people who has difficulty in sleeping is up to 27%. Detecting the cause of these sleep disorders needs an elaborate analysis of the physiological signals of different sleep stages. Analyzing and comparing the brain electrical activity mapping energy difference of normal subjects and subjects who have the disease of nocturnal frontal lobe epilepsy is introduced in this study. The brain electrical activity mapping is from the independent component analysis (ICA) of the Electroencephalograph (EEG) waveform. The EEG data set is coming from the CAP sleep database. The control group uses the data of n3, n10, and n11. The experimental group uses the data of nfle1, nfle2, and nfle3. The EEGLAB, a toolbox in MATLAB, is used to preprocess the EEG waveform and locate the area where signals are generated in the brain. The preprocessing steps include channel locations, selecting data, filtering, re-referencing the data, ICA, and artifact rejection. After the preprocessing, there are 13 electrodes retained and the energy difference of the brain electrical activity mapping will be compared between the control group and the experimental group by observation.

Comparative Approach to Detect Nocturnal Frontal Lobe Epilepsy Sleep Disorder through Frequency spectrum and its Energy Levels

EEG data has proved to reflect the activities of the brain over all the sections with respect to human activities. It is useful in both cases that are in awakening states and in the sleep stage. Most of brain disease is due to the deterioration of brain cells. In this research work, EEG data is used to generate some frequency-based energy level features for NFLE (Nocturnal Frontal Lobe Epilepsy) patients and normal persons. After experimentation, it is found that the percentage of energy levels may be considered as a parameter to distinguish healthily and defected EEG data due to sleep disorder. During this study, it is also found that S0 is proved to be helpful in the diagnosis of NFLE sleep disorder because in this stage the percentage energy level is to be high for alpha waves for patients having a sleep disorder. The results show that the average percentage energy level in NFLE patient is 0.009872 while in normal cases it is 0.0010148.

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG.

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels’ feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2–F4, C4–A1, F4–C4), which is in line with the CAP protocol to ensure multiple channels’ arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

GTransU-CAP: Automatic labeling for cyclic alternating patterns in sleep EEG using gated transformer-based U-Net framework

Cyclic alternating pattern (CAP) sequences are composed of cycles of alternate activation phases (A-phases) and background phases. CAP A-phases can be further divided into three subtypes, which act as important bio-markers of sleep instability and are also associated with identifiable sleep pathologies. Thus, its accurate detection and identification is of great clinical interest and significance. To release the burden of sleep experts who manually perform this labeling task, several automatic detectors have been proposed, yet the characteristics of CAP have not been fully exploited to achieve a satisfactory performance. In this paper, we propose an automated method to detect A-phases and their subtypes using Transformer-based U-Net framework. In light of the long-span duration of A-phases, our method has intrinsic advantages as U-Net extracts local information while Transformer module provides global dependencies. We also use a curriculum-learning based training strategy to further improve the performance. The method is validated on the publicly available CAP Sleep Database. It obtains average F1 scores of 67.78% and 72.16% on 16 healthy subjects and 30 patients with nocturnal frontal lobe epilepsy respectively for A-phase detection, and the average macro F1-score is 59.5% for multi-class subtype classification. Compared with state-of-the-art methods, the proposed method achieves superior performance in these two CAP labeling tasks.

Automated classification of cyclic alternating pattern sleep phases in healthy and sleep-disordered subjects using convolutional neural network

Sleep contributes to more than a third of a person's life, making sleep monitoring essential for overall well-being. Cyclic alternating patterns (CAP) are crucial in monitoring sleep quality and associated illnesses such as insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, etc. However, traditionally medical specialists practice manual division techniques of CAP phases which are sensitive to human weariness and inaccuracies. This might result in a false sleep stage diagnosis. This study proposes an automated approach using a deep learning model based on a 1-dimensional convolutional neural network for classifying CAP phases (A and B). The proposed model uses single-channel standardized electroencephalogram (EEG) recordings provided by the CAP sleep database. The model was created with the help of healthy participants and patients suffering from five distinct sleep disorders, which includes narcolepsy, rapid eye movement behaviour disorder (RBD), periodic leg movement disorder (PLM), NFLE, and insomnia. The developed model has achieved the highest automated classification accuracy of 78.84% for the healthy dataset and 82.21%, 79.48%, 78.73%, 76.68%, and 70.88% for narcolepsy, RBD, PLM, NFLE, and insomnia subjects, respectively in categorizing phases A and B. The proposed approach can help medical professionals monitor sleep and examine a person's brain stability.

A novel missense creatine mutant of CaBP4, c.464G>A (p.G155D), associated with autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE), reduces the expression of CaBP4.

BackgroundCaBP4 encodes Ca2+-binding protein 4, a neuronal Ca2+-binding protein that participates in many cellular processes by regulating the concentration of free Ca2+ ions. De novo CaBP4 variants have been identified as a cause of congenital stationary night blindness (CSNB). However, we recently reported a 4-generation pedigree with 11 individuals diagnosed with autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) that were validated with only one novel missense mutation, c.464G>A (p.G155D), in CaBP4. De novo CaBP4 variants have never been reported to be related with ADNFLE. This study aimed to identify whether c.464G>A (p.G155D) in CaBP4 reduced the expression of CaBP4.MethodsIn vitro experiments using recombinant protein expressed in human neuron cells were utilized in this study. Real-time polymerase chain reaction (RT-PCR) was performed to evaluate the effect of c.464G>A on CaBP4 mRNA expression. Western blot was performed to assess the effect of c.464G>A on CaBP4 protein expression.ResultsAccording to the RT-PCR and Western blot results, c.464G>A (p.G155D) was associated with an increased expression of CaBP4 mRNA and a reduced expression of CaBP4 protein.ConclusionsThese results reveal that c.464G>A (p.G155D) in CaBP4 reduced the expression of CaBP4 by reducing the stability of the CaBP4 protein. Mutations in the CaBP4 gene may be associated with ADNFLE.

Nocturnal frontal lobe epilepsy presenting as sleepwalking episodes and restless leg syndrome-like symptoms

Nocturnal frontal lobe epilepsy (NFLE) is characterized by seizures with complex motor behaviors arising mainly during sleep. Varied clinical presentations and nonspecific electroencephalogram findings make it difficult to distinguish NFLE from other nocturnal paroxysmal events such as nonrapid eye movement (NREM) parasomnias and restless leg syndrome (RLS). Here, we present a case of NFLE, which had a complex clinical presentation resembling features suggestive of RLS and NREM parasomnia and posed a diagnostic challenge. Initiation of carbamazepine as the anti-epileptic led to rapid resolution of symptoms.

AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram.

Healthy sleep is an essential physiological process for every individual to live a healthy life. Many sleep disorders both destroy the quality and decrease the duration of sleep. Thus, a convenient and accurate detection or classification method is important for screening and identifying sleep disorders. In this study, we proposed an AI-enabled algorithm for the automatic classification of sleep disorders based on a single-lead electrocardiogram (ECG). An AI-enabled algorithm—named a sleep disorder network (SDN)—was designed for automatic classification of four major sleep disorders, namely insomnia (INS), periodic leg movement (PLM), REM sleep behavior disorder (RBD), and nocturnal frontal-lobe epilepsy (NFE). The SDN was constructed using deep convolutional neural networks that can extract and analyze the complex and cyclic rhythm of sleep disorders that affect ECG patterns. The SDN consists of five layers, a 1D convolutional layer, and is optimized via dropout and batch normalization. The single-lead ECG signal was extracted from the 35 subjects with the control (CNT) and the four sleep disorder groups (seven subjects of each group) in the CAP Sleep Database. The ECG signal was pre-processed, segmented at 30 s intervals, and divided into the training, validation, and test sets consisting of 74,135, 18,534, and 23,168 segments, respectively. The constructed SDN was trained and evaluated using the CAP Sleep Database, which contains not only data on sleep disorders, but also data of the control group. The proposed SDN algorithm for the automatic classification of sleep disorders based on a single-lead ECG showed very high performances. We achieved F1 scores of 99.0%, 97.0%, 97.0%, 95.0%, and 98.0% for the CNT, INS, PLM, RBD, and NFE groups, respectively. We proposed an AI-enabled method for the automatic classification of sleep disorders based on a single-lead ECG signal. In addition, it represents the possibility of the sleep disorder classification using ECG only. The SDN can be a useful tool or an alternative screening method based on single-lead ECGs for sleep monitoring and screening.

The search for statistical patterns of pathological activity in human EEG signals in focal epilepsy

Modern data science faces a lot of challenges, one of which is the search for diagnostic criteria for neurological diseases. New methods of statistical analysis are actively applied in the field of biophysics to solve this issue. In this paper we apply the Memory Functions Formalism to analyze electroencephalogram signal recordings in the sleeping state of 8 healthy subjects and 19 patients with nocturnal lobe epilepsy. We observe the considerable difference of statistical memory effects and fractal properties at the pathology in comparison with the control group. Furthermore, we reveal significant alterations in brain rhythms at power spectra of statistical memory functions for two groups of subjects. As a result, we show that the application of the statistical analysis methodology of bioelectrical brain cortex activity recordings, after appropriate verification, can be useful in the search for diagnostic criteria of nocturnal frontal lobe epilepsy.

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals.

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.