Epileptic seizure is a neurological disorder affecting 50 million people worldwide. If the seizure goes unrecognized, it may lead to fatal accidents. It is important to predict seizure and initiate preventive interventions to reduce the risks. It can be used to alert the patient, relatives, clinicians and family members can record a video that will guide the neurologist to refine the treatment. Electroencephalogram (EEG) is a standard method for diagnosis. But it is less suitable in an ambulatory setting. A more convenient alternative is to wear an electrocardiogram (ECG) belt around the chest.We explore the possibility of designing an ECG-based patient warning system. We use a deep neural network (DNN) as a baseline system with 65.33%, 64.47%, 67.75%, and 0.6752 of accuracy, sensitivity, specificity, and area under the receiver operating characteristics (AUROC) respectively. We improved the performance with convolutional neural network (CNN) and signal decomposition methods using empirical mode decomposition (EMD) and variational mode decomposition (VMD). We empirically obtained the decision weights for the bottleneck features derived from the best-performing EMD and VMD modes. In the decision fusion, the accuracy, sensitivity, specificity, and AUROC is 84.68%, 69.60%, 93.63%, and 0.8162 respectively which showed an improvement of 19.35%, 5.13%, 25.88%, and 0.141 respectively from the baseline system.

Improving the Performance of ECG-based Epileptic Seizure Prediction

There is a need to develop objective and real-time postoperative pain assessment methods in perioperative medicine. Few studies have evaluated the relationship between pain severity and temporal changes of physiological signals in actual postoperative patients. In this study, we developed a machine learning model which was trained from intravenous patient-controlled analgesia (IV-PCA) records and electrocardiogram (ECG) of postoperative patients to predict pain exacerbation. A self-attentive autoencoder (SA-AE) model achieved 54% of sensitivity and a 1.76 times/h of false positive rate.Clinical relevance― We proposed a novel method for evaluating postoperative pain in real time and demonstrated the possibility of predicting pain exacerbation. The proposed method would realize the automatic administration of analgesics and the optimization of opioid doses.

Prediction model of postoperative pain exacerbation using an intravenous patient-controlled analgesia device and a wearable electrocardiogram sensor

Epilepsy and Prediction Devices

OBJECTIVES
Early prediction of epileptic seizures can help reduce morbidity and mortality. In this work, we explore using electrocardiographic (ECG) signal as input to a seizure prediction system and note that the performance can be improved by using selected signal processing techniques.


METHODS
We used frequency domain analysis with a deep neural network backend for all our experiments in this work. We further analysed the effect of the proposed system for different seizure semiologies and prediction horizons. We explored refining the signal using signal processing to enhance the system's performance.


RESULTS
Our final system using the Temple University Hospital's Seizure (TUHSZ) corpus gave an overall prediction accuracy of 84.02 %, sensitivity of 87.59 %, specificity of 81.9 %, and an area under the receiver operating characteristic curve (AUROC) of 0.9112. Notably, these results surpassed the state-of-the-art outcomes reported using the TUHSZ database; all findings are statistically significant. We also validated our study using the Siena scalp EEG database. Using the frequency domain data, our baseline system gave a performance of 75.17 %, 79.17 %, 70.04 % and 0.82 for prediction accuracy, sensitivity, specificity and AUROC, respectively. After selecting the optimal frequency band of 0.8-15 Hz, we obtained a performance of 80.49 %, 89.51 %, 75.23 % and 0.89 for prediction accuracy, sensitivity, specificity and AUROC, respectively which is an improvement of 5.32 %, 10.34 %, 5.19 % and 0.08 for prediction accuracy, sensitivity, specificity and AUROC, respectively.


CONCLUSIONS
The seizure information in ECG is concentrated in a narrow frequency band. Identifying and selecting that band can help improve the performance of seizure detection and prediction.


SIGNIFICANCE
EEG is susceptible to artefacts and is not preferred in a low-cost ambulatory device. ECG can be used in wearable devices (like chest bands) and is feasible for developing a low-cost ambulatory device for seizure prediction. Early seizure prediction can provide patients and clinicians with the required alert to take necessary precautions and prevent a fatality, significantly improving the patient's quality of life.

AI in ECG: Validating an ambulatory semiology labeller and predictor

Advancements in deep learning speech representations have facilitated the effective use of extensive unlabeled speech datasets for Parkinson’s disease (PD) modeling with minimal annotated data. This study employs the non-fine-tuned wav2vec 1.0 architecture to develop machine learning models for PD speech diagnosis tasks, such as cross-database classification and regression to predict demographic and articulation characteristics. The primary aim is to analyze overlapping components within the embeddings on both classification and regression tasks, investigating whether latent speech representations in PD are shared across models, particularly for related tasks. Firstly, evaluation using three multi-language PD datasets showed that wav2vec accurately detected PD based on speech, outperforming feature extraction using mel-frequency cepstral coefficients in the proposed cross-database classification scenarios. In cross-database scenarios using Italian and English-read texts, wav2vec demonstrated performance comparable to intra-dataset evaluations. We also compared our cross-database findings against those of other related studies. Secondly, wav2vec proved effective in regression, modeling various quantitative speech characteristics related to articulation and aging. Ultimately, subsequent analysis of important features examined the presence of significant overlaps between classification and regression models. The feature importance experiments discovered shared features across trained models, with increased sharing for related tasks, further suggesting that wav2vec contributes to improved generalizability. The study proposes wav2vec embeddings as a next promising step toward a speech-based universal model to assist in the evaluation of PD. 

Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson’s Disease Detection and Speech Features Extraction

—Prediction of epileptic seizures may improve the Quality of Life of patients. In this study, we developed a seizure prediction machine learning model using R-R interval (RRI) data of epileptic patients, and a Self-attentive Autoencoder (SA-AE) was used to efﬁciently train the model and predict seizures. The constructed model achieved the sensitivity of 71 % and FP rate of 0.90 times/h. We will further improve the model performance and explore the causes of patients with poor seizure prediction performance. Clinical relevance ― Seizure prediction in epilepsy can ease a patient’s anxiety about seizures. In addition, RRI is easy to measure by wearable devices, which can realize an epileptic seizure prediction system that can be used in daily life.

R-R Intervals based Epileptic Seizure Prediction Algorithm Utilizing Self-attentive Autoencoder

Abstract Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data. 

/pdf/development-of-an-epileptic-seizure-prediction-algorithm-3bxzunun.pdf

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Single nucleotide variants (SNVs) affecting the first nucleotide G of an exon (Fex-SNVs) identified in various diseases are mostly recognized as missense or nonsense variants. Their effect on pre-mRNA splicing has been seldom analyzed, and no curated database is available. We previously reported that Fex-SNVs affect splicing when the length of the polypyrimidine tract is short or degenerate. However, we cannot readily predict the splicing effects of Fex-SNVs. We here scrutinized the available literature and identified 106 splicing-affecting Fex-SNVs based on experimental evidence. We similarly identified 106 neutral Fex-SNVs in the dbSNP database with a global minor allele frequency (MAF) of more than 0.01 and less than 0.50. We extracted 115 features representing the strength of splicing cis-elements and developed machine-learning models with support vector machine, random forest, and gradient boosting to discriminate splicing-affecting and neutral Fex-SNVs. Gradient boosting-based LightGBM outperformed the other two models, and the length and nucleotide compositions of the polypyrimidine tract played critical roles in the discrimination. Recursive feature elimination showed that the LightGBM model using 15 features achieved the best performance with an accuracy of 0.80 ± 0.12 (mean and SD), a Matthews Correlation Coefficient (MCC) of 0.57 ± 0.15, an area under the curve of the receiver operating characteristics curve (AUROC) of 0.86 ± 0.08, and an area under the curve of the precision–recall curve (AUPRC) of 0.87 ± 0.09 using a 10-fold cross-validation. We developed a web service program, named FexSplice that accepts a genomic coordinate either on GRCh37/hg19 or GRCh38/hg38 and returns a predicted probability of aberrant splicing of A, C, and T variants.

FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon

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R-R Intervals based Epileptic Seizure Prediction Algorithm Utilizing Self-attentive Autoencoder

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

FexSplice: A LightGBM-Based Model for Predicting the Splicing Effect of a Single Nucleotide Variant Affecting the First Nucleotide G of an Exon