Heart Rate and its Variability from Short-term ECG Recordings as Biomarkers for Detecting Mild Cognitive Impairment in Indian Population

• URL: http://arxiv.org/abs/2408.02296v1
• Date: Mon, 5 Aug 2024 08:14:04 GMT
• Title: Heart Rate and its Variability from Short-term ECG Recordings as Biomarkers for Detecting Mild Cognitive Impairment in Indian Population
• Abstract summary: We design a complete signal processing pipeline to detect the R-wave peaks and compute HR and HRV features from ECG recordings of 10 seconds. Individuals with MCI are observed to have comparatively higher HR than healthy subjects.
• Score: 0.5874782446136914
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) can reflect autonomic dysfunction associated with neurodegeneration. We investigate the influence of Mild Cognitive Impairment (MCI) on HR and its variability measures in the Indian population by designing a complete signal processing pipeline to detect the R-wave peaks and compute HR and HRV features from ECG recordings of 10 seconds, for point-of-care applications. The study cohort involves 297 urban participants, among which 48.48% are male and 51.51% are female. From the Addenbrooke's Cognitive Examination-III (ACE-III), MCI is detected in 19.19% of participants and the rest, 80.8% of them are cognitively healthy. Statistical features like central tendency (mean and root mean square (RMS) of the Normal-to-Normal (NN) intervals) and dispersion (standard deviation (SD) of all NN intervals (SDNN) and root mean square of successive differences of NN intervals (RMSSD)) of beat-to-beat intervals are computed. The Wilcoxon rank sum test reveals that mean of NN intervals (p = 0.0021), the RMS of NN intervals (p = 0.0014), the SDNN (p = 0.0192) and the RMSSD (p = 0.0206) values differ significantly between MCI and non-MCI classes, for a level of significance, 0.05. Machine learning classifiers like, Support Vector Machine (SVM), Discriminant Analysis (DA) and Naive Bayes (NB) driven by mean NN intervals, RMS, SDNN and RMSSD, show a high accuracy of 80.80% on each individual feature input. Individuals with MCI are observed to have comparatively higher HR than healthy subjects. HR and its variability can be considered as potential biomarkers for detecting MCI.

Related papers

• Analysis of Autonomic Regulation in Cancer Survivors During Daily Physical Activity: A Real-World Wearable ECG Study [1.2178992475191555]
  This study investigates heart rate (HR) and heart rate variability (HRV) responses to physical activity in breast cancer survivors using wearable electrocardiogram (ECG) data collected in real-world settings.
  arXiv  Detail & Related papers  (2026-06-22T15:10:17Z)
• CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors [44.01195029438923]
  We introduce CoDaS (AI Co-Data-Scientist), a system that structures biomarker discovery as an iterative process.<n>Across three cohorts totaling 9,279 participantobservations, CoDaS identified 41 candidate digital for mental health and 25 for metabolic outcomes.<n>CoDaS surfaced circadian instability-related features in both datasets reflected in sleep duration variability.
  arXiv  Detail & Related papers  (2026-04-16T04:47:55Z)
• NeuroRAD-FM: A Foundation Model for Neuro-Oncology with Distributionally Robust Training [12.168374910250572]
  Existing foundation models (FMs) perform poorly in predicting uncommon molecular markers.<n>We developed a neuro-oncology specific FM with a distributionally robust loss function, enabling accurate estimation of tumor phenotypes.<n>For survival, c-index improved at all sites: CUIMC 0.592 to 0.597, UPenn 0.647 to 0.672, UCSF 0.600 to 0.627.
  arXiv  Detail & Related papers  (2025-09-18T20:43:08Z)
• Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors [2.208475400165877]
  Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure spikes in individuals with spinal cord injury (SCI)<n>This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors.
  arXiv  Detail & Related papers  (2025-07-23T21:18:23Z)
• Physiological-Model-Based Neural Network for Heart Rate Estimation during Daily Physical Activities [3.5717820799814306]
  Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes.<n>The estimation of individualized heart rate (HR) serves as a dynamic digital twin, enabling precise tracking of cardiac health biomarkers.<n>This study introduces a novel physiological-model-based neural network (PMB-NN) framework for HR estimation based on oxygen uptake (VO2) data during daily physical activities.
  arXiv  Detail & Related papers  (2025-06-11T19:48:08Z)
• Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder [60.84344168388442]
  Language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD.<n>We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong.<n>The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
  arXiv  Detail & Related papers  (2025-06-10T16:58:47Z)
• Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV [49.1574468325115]
  This study explores multiple ML approaches for predicting LOS in ICU specifically for the patients with neurological diseases based on the MIMIC-IV dataset.<n>The evaluated models include classic ML algorithms (K-Nearest Neighbors, Random Forest, XGBoost and CatBoost) and Neural Networks (LSTM, BERT and Temporal Fusion Transformer)
  arXiv  Detail & Related papers  (2025-05-23T14:06:42Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring. Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals [0.5870004969741518]
  This study employs data-driven machine learning (ML) for time-frequency analysis of local neural activity within key functional networks. We obtain time-frequency features based on rs-fMRI BOLD signals from the default mode network (DMN), salience network (SN), and executive control network (ECN) The DMN and SN show the most discriminative power, significantly outperforming chance baselines with mean F1 scores of 0.7097 and 0.7018, respectively.
  arXiv  Detail & Related papers  (2024-10-24T20:30:14Z)
• 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)
• Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment [2.474908349649168]
  Mild cognitive impairment (MCI) is characterized by subtle changes in cognitive functions, often associated with disruptions in brain connectivity. The present study introduces a novel fine-grained analysis to examine topological alterations in neurodegeneration pertaining to six different brain networks of subjects.
  arXiv  Detail & Related papers  (2024-08-28T09:01:55Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Towards Bloodless Potassium Measurement from ECG using Neuro-Fuzzy Systems [0.0]
  Potassium disorders are generally asymptomatic, potentially lethal, and common in patients with renal or cardiac disease. The morphology of the electrocardiogram (ECG) signal is very sensitive to the changes in potassium ions. This paper introduces a new system for ECG-based potassium measurement.
  arXiv  Detail & Related papers  (2023-12-28T11:28:41Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation [0.0]
  Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG) Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population.
  arXiv  Detail & Related papers  (2022-07-17T13:06:39Z)
• Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification [48.42347515853289]
  Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD) In this study, we propose an automatic method of determining the duration of TMS induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations.
  arXiv  Detail & Related papers  (2022-04-29T19:27:06Z)
• 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)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity [39.57255380551913]
  We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect Alzheimer's dementia (AD) and its severity. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression.
  arXiv  Detail & Related papers  (2020-08-30T21:47:26Z)
• 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)

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.