CNN-LSTM
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BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can have a cascading effect on the body, potentially leading to complications in various organs, including the heart, brain, and lungs. Due to the potential for these complications, early and accurate detection of SA is critical. Electrocardiograms (ECG), due to their ability to continuously monitor heart rhythms and detect subtle changes in cardiac activity, such as heart rate variability and arrhythmias, which are often linked to sleep disruptions, have become crucial in identifying individuals at risk for SA. METHOD: In this study, we propose a hybrid neural network model named CNN-Transformer-LSTM that uses a single-lead ECG signal to detect SA automatically. This method captures spatial and temporal features in the ECG data to improve classification performance. Our model utilizes RR intervals (RRI) and R-peak signals derived from ECG data as input and then classifies SA and normal states on a per-segment and per-recording basis. We evaluated the model using the Physionet Apnea-ECG dataset, consisting of 70 single-lead ECG recordings annotated by medical professionals, and the UCD St. Vincent's University Hospital's sleep apnea database (UCDDB) containing polysomnogram records from 25 patients. RESULTS: Our model achieved an accuracy of 91.6% for per-segment classification on the Physionet Apnea-ECG dataset using hold-out validation and the highest accuracy of 94.1% using five-fold cross-validation. As for per-recording classification, our model achieved an accuracy of 100% and the highest correlation coefficient value of 0.9996 using five-fold cross-validation. On the UCDDB dataset, our model achieved an accuracy of 99.37% on the reduced dataset excluding 4 patients and 98.34% on the full dataset. Compared to previous works, our model improved the per-segment classification accuracy by nearly 3% over the existing best result, thereby demonstrating that our model outperforms existing state-of-the-art methods in accurately detecting SA from a single-lead ECG signal. CONCLUSION: These results highlight the effectiveness of the CNN-Transformer-LSTM model for SA detection and its potential to be used in SA detection devices for home health care and clinical settings.
- Klíčová slova
- CNN-Transformer-LSTM, Deep learning, Detection, Electrocardiogram, Sleep apnea,
- MeSH
- dospělí MeSH
- elektrokardiografie * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování signálu * MeSH
- syndromy spánkové apnoe * diagnóza patofyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Long terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs. RESULTS: We used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges. CONCLUSIONS: Our results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5'- and 3'- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.
- Klíčová slova
- CNN-LSTM, DNABERT, Deep learning, Eukaryote, Regulatory mechanisms, Repeat, SHAP score, Sequence analysis, TFBS, Transcription factor binding sites, Transposable elements,
- Publikační typ
- časopisecké články MeSH
The electroencephalogram (EEG) is a cornerstone of neurophysiological research and clinical neurology. Historically, the classification of EEG as showing normal physiological or abnormal pathological activity has been performed by expert visual review. The potential value of unbiased, automated EEG classification has long been recognized, and in recent years the application of machine learning methods has received significant attention. A variety of solutions using convolutional neural networks (CNN) for EEG classification have emerged with impressive results. However, interpretation of CNN results and their connection with underlying basic electrophysiology has been unclear. This paper proposes a CNN architecture, which enables interpretation of intracranial EEG (iEEG) transients driving classification of brain activity as normal, pathological or artifactual. The goal is accomplished using CNN with long short-term memory (LSTM). We show that the method allows the visualization of iEEG graphoelements with the highest contribution to the final classification result using a classification heatmap and thus enables review of the raw iEEG data and interpret the decision of the model by electrophysiology means.
- MeSH
- artefakty MeSH
- datové soubory jako téma MeSH
- deep learning * MeSH
- elektroencefalografie klasifikace přístrojové vybavení metody MeSH
- lidé MeSH
- ROC křivka MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- validační studie MeSH
INTRODUCTION: The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. OBJECTIVE: This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. METHODS: We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. RESULTS: Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. CONCLUSION: Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.
- Klíčová slova
- Blockchain, Data modality and standardization, Digital twin, IIoWT, Transparency, Water management,
- Publikační typ
- časopisecké články MeSH
During the lockdown of universities and the COVID-Pandemic most students were restricted to their homes. Novel and instigating teaching methods were required to improve the learning experience and so recent implementations of the annual PhysioNet/Computing in Cardiology (CinC) Challenges posed as a reference. For over 20 years, the challenges have proven repeatedly to be of immense educational value, besides leading to technological advances for specific problems. In this paper, we report results from the class 'Artificial Intelligence in Medicine Challenge', which was implemented as an online project seminar at Technical University Darmstadt, Germany, and which was heavily inspired by the PhysioNet/CinC Challenge 2017 'AF Classification from a Short Single Lead ECG Recording'. Atrial fibrillation is a common cardiac disease and often remains undetected. Therefore, we selected the two most promising models of the course and give an insight into the Transformer-based DualNet architecture as well as into the CNN-LSTM-based model and finally a detailed analysis for both. In particular, we show the model performance results of our internal scoring process for all submitted models and the near state-of-the-art model performance for the two named models on the official 2017 challenge test set. Several teams were able to achieve F1scores above/close to 90% on a hidden test-set of Holter recordings. We highlight themes commonly observed among participants, and report the results from the self-assessed student evaluation. Finally, the self-assessment of the students reported a notable increase in machine learning knowledge.
- Klíčová slova
- atrial fibrillation, deep learning, electrocardiogram, gamification,
- MeSH
- algoritmy MeSH
- COVID-19 * diagnóza MeSH
- elektrokardiografie metody MeSH
- fibrilace síní * diagnóza MeSH
- kontrola infekčních nemocí MeSH
- lidé MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets-NREL WIND, EMD WIND, WWSIS, and ERCOT GRID-and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil's Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications.
