Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography

• URL: http://arxiv.org/abs/2412.15947v3
• Date: Sat, 09 Aug 2025 02:59:43 GMT
• Title: Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
• Authors: Andrew H. Zhang, Alex He-Mo, Richard Fei Yin, Chunlin Li, Yuzhi Tang, Dharmendra Gurve, Veronique van der Horst, Aron S. Buchman, Nasim Montazeri Ghahjaverestan, Maged Goubran, Bo Wang, Andrew S. P. Lim,
• Abstract summary: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One.<n>We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography.<n>We trained a Mamba-based recurrent neural network architecture on these recordings.
• Score: 3.7428541180163126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Study Objectives: We investigate a Mamba-based deep learning approach for sleep staging on signals from ANNE One (Sibel Health, Evanston, IL), a non-intrusive dual-module wireless wearable system measuring chest electrocardiography (ECG), triaxial accelerometry, and chest temperature, and finger photoplethysmography and finger temperature. Methods: We obtained wearable sensor recordings from 357 adults undergoing concurrent polysomnography (PSG) at a tertiary care sleep lab. Each PSG recording was manually scored and these annotations served as ground truth labels for training and evaluation of our models. PSG and wearable sensor data were automatically aligned using their ECG channels with manual confirmation by visual inspection. We trained a Mamba-based recurrent neural network architecture on these recordings. Ensembling of model variants with similar architectures was performed. Results: After ensembling, the model attains a 3-class (wake, non rapid eye movement [NREM] sleep, rapid eye movement [REM] sleep) balanced accuracy of 84.02%, F1 score of 84.23%, Cohen's $\kappa$ of 72.89%, and a Matthews correlation coefficient (MCC) score of 73.00%; a 4-class (wake, light NREM [N1/N2], deep NREM [N3], REM) balanced accuracy of 75.30%, F1 score of 74.10%, Cohen's $\kappa$ of 61.51%, and MCC score of 61.95%; a 5-class (wake, N1, N2, N3, REM) balanced accuracy of 65.11%, F1 score of 66.15%, Cohen's $\kappa$ of 53.23%, MCC score of 54.38%. Conclusions: Our Mamba-based deep learning model can successfully infer major sleep stages from the ANNE One, a wearable system without electroencephalography (EEG), and can be applied to data from adults attending a tertiary care sleep clinic.

Related papers

• Sleep Apnea Detection on a Wireless Multimodal Wearable Device Without Oxygen Flow Using a Mamba-based Deep Learning Approach [0.0]
  We present and evaluate a Mamba-based deep-learning model for diagnosis and event-level characterization of sleep disordered breathing based on signals from the ANNE One.
  arXiv  Detail & Related papers  (2025-11-30T17:21:44Z)
• Detecting Blinks in Healthy and Parkinson's EEG: A Deep Learning Perspective [0.0]
  We evaluate various deep learning models for segmenting EEG signals into involuntary blinks and non-blinks.<n>We present a pipeline for blink detection using 1, 3, or 5 frontal EEG electrodes.
  arXiv  Detail & Related papers  (2025-09-05T09:17:55Z)
• MT-NAM: An Efficient and Adaptive Model for Epileptic Seizure Detection [51.87482627771981]
  Micro Tree-based NAM (MT-NAM) is a distilled model based on the recently proposed Neural Additive Models (NAM) MT-NAM achieves a remarkable 100$times$ improvement in inference speed compared to standard NAM, without compromising accuracy. We evaluate our approach on the CHB-MIT scalp EEG dataset, which includes recordings from 24 patients with varying numbers of sessions and seizures.
  arXiv  Detail & Related papers  (2025-03-11T10:14:53Z)
• ECG-SleepNet: Deep Learning-Based Comprehensive Sleep Stage Classification Using ECG Signals [4.070551979222657]
  This study proposes a novel three-stage approach for sleep stage classification using ECG signals.<n>In the first phase, we estimate key features using Feature Imitating Networks (FINs) to achieve higher accuracy and faster convergence.<n>The second phase focuses on identifying the N1 sleep stage through the time-frequency representation of ECG signals.<n>The third phase integrates models from the previous stages and employs a Kolmogorov-Arnold Network (KAN) to classify five distinct sleep stages.
  arXiv  Detail & Related papers  (2024-12-02T19:31:25Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Multi-centric AI Model for Unruptured Intracranial Aneurysm Detection and Volumetric Segmentation in 3D TOF-MRI [6.397650339311053]
  We developed an open-source nnU-Net-based AI model for combined detection and segmentation of unruptured intracranial aneurysms (UICA) in 3D TOF-MRI. Four distinct training datasets were created, and the nnU-Net framework was used for model development. The primary model showed 85% sensitivity and 0.23 FP/case rate, outperforming the ADAM-challenge winner (61%) and a nnU-Net trained on ADAM data (51%) in sensitivity.
  arXiv  Detail & Related papers  (2024-08-30T08:57:04Z)
• SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals [17.416001617612658]
  Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We developed SleepFM, the first multi-modal foundation model for sleep analysis. We show that a novel leave-one-out approach for contrastive learning significantly improves downstream task performance.
  arXiv  Detail & Related papers  (2024-05-28T02:43:53Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Sleep Staging Based on Serialized Dual Attention Network [0.0]
  We propose a deep learning model SDAN based on raw EEG. It serially combines the channel attention and spatial attention mechanisms to filter and highlight key information. It achieves excellent results in the N1 sleep stage compared to other methods.
  arXiv  Detail & Related papers  (2021-07-18T13:18:12Z)
• A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections [46.414990784180546]
  Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique. CNNs require large annotated datasets for training. Self-supervised learning strategies can leverage unlabeled data for training.
  arXiv  Detail & Related papers  (2021-05-24T12:27:06Z)
• Estimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study [0.0]
  This study presents a method to detect atrial fibrillation with lead-I ECGs using artificial intelligence. The aim of the study is to compare the accuracy of the diagnoses estimated by cardiologists and artificial intelligence over lead-I ECGs.
  arXiv  Detail & Related papers  (2021-04-15T12:50:16Z)
• MSED: a multi-modal sleep event detection model for clinical sleep analysis [62.997667081978825]
  We designed a single deep neural network architecture to jointly detect sleep events in a polysomnogram. The performance of the model was quantified by F1, precision, and recall scores, and by correlating index values to clinical values.
  arXiv  Detail & Related papers  (2021-01-07T13:08:44Z)
• Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals [0.0]
  The regulation of the autonomic nervous system changes with the sleep stages. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds.
  arXiv  Detail & Related papers  (2020-08-07T21:43:46Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network [0.19686770963118383]
  The study presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. This demonstrates the model can identify the presence of Apnoea with a high degree of accuracy.
  arXiv  Detail & Related papers  (2020-02-03T15:47:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.