Traditional vehicle fault diagnosis methods rely heavily on the expertise of mechanics or diagnostic tools available at service centers, which can be costly, time-consuming, and may not always provide accurate results. This study presents a comprehensive vehicle fault diagnosis framework, which utilized Mel-Frequency Cepstral Coefficients (MFCCs), Discrete Wavelet Transform (DWT)-based features, and the Extreme Learning Machine (ELM) classifier. To address the limitations of previous works, the proposed framework leverages a large, diverse dataset encompassing various vehicle models and real-world operating conditions. Significantly improved robustness and generalizability of the fault diagnosis system were achieved. The results of the experiments demonstrate the superiority of the MFCC-based features combined with the ELM classifier, achieving the highest performance metrics in terms of accuracy, precision, recall, F1-score, macro F1-score, and weighted F1-score, which are 92.17%, 92.24%, 92.22%, 92.10%, and 92.06%, respectively. Slightly lower performance was obtained while employing the DWT-based features compared to employing MFCC-based features. Additionally, frequency analysis was conducted to identify specific frequency bins, which are the most indicative of different fault types in providing valuable guidance for future diagnostic efforts. Overall, the proposed framework provides a reliable and practical solution for accurate vehicle fault detection, paving the way for future advancements in automotive diagnostics.

Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results. By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.

Accurate sleep stage scoring is crucial for diagnosing and treating sleep disorders, yet traditional manual methods are time-consuming and susceptible to variability. While recent advancements in machine learning and deep learning have enhanced automated sleep stage detection, many approaches still rely on handcrafted features and encounter limitations when processing full-night data. In this paper, we introduce a novel many-to-many classification framework that leverages a temporal focal modulation network for efficient and accurate sleep stage scoring. Our model, SleepFocalNet, processes full-night single-channel EEG signals and predicts sleep stages for all epochs simultaneously. SleepFocalNet is composed of three key components: a convolution block for local feature extraction, a focal modulation block for long-range temporal modeling, and a classification block for final predictions. We evaluated SleepFocalNet on Sleep Heart Health Study (SHHS), SleepEDF-20, and SleepEDF-78 datasets, achieving state-of-the-art performance. On SHHS, SleepFocalNet attained an accuracy of 0.888 and an F1-score of 0.815. On SleepEDF-20, it obtained an accuracy of 0.885 and an F1-score of 0.836. On SleepEDF-78, it outperformed other models with an accuracy of 0.855 and an F1-score of 0.800. This study represents the first application of temporal focal modulation networks in sleep stage scoring. Additionally, we conducted an extensive analysis of various network configurations to assess the impact of different architectural choices on performance. The results validate the potential of our approach to enhance the reliability and scalability of automated sleep stage scoring, offering a robust alternative to existing methods.

Objective. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods. Approach. This study proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) spectrograms, integrating structured state space models (S4) for temporal modeling. The framework consists of a convolutional neural network module for local feature extraction, an S4 module for capturing long-range dependencies, and a classification module for final predictions. Main results. The model was trained and evaluated on the Apnea-ECG dataset, achieving an accuracy of 0.933, an F1-score of 0.912, a sensitivity of 0.916, and a specificity of 0.944, outperforming most prior studies while maintaining computational efficiency. Significance. Compared to existing methods, ModelS4Apnea provides high classification performance with significantly fewer trainable parameters than long short-term memory-based models, reducing training time and memory consumption. The model’s ability to aggregate segment-level predictions enabled perfect per-recording classification, demonstrating its robustness in diagnosing sleep apnea across entire recordings. Moreover, its low memory footprint and fast inference speed make it well-suited for wearable devices, home-based monitoring, and clinical applications, offering a scalable and efficient solution for automated sleep apnea detection. Future work may explore multi-modal data integration, real-world deployment, and further optimizations to enhance its clinical applicability and reliability.