Hidden Markov modelling (HMM) is a powerful stochastic modelling technique that has been successfully applied to automatic speech recognition problems. We are currently investigating the application of HMM to electrocardiographic signal analysis with the goal of improving ambulatory ECG analysis. The HMM approach specifies a Markov chain to model a "hidden" sequence that in this case is the underlying state of the heart. Each state of the Markov chain has an associated output function that describes the statistical characteristics of measurement samples generated during that state. Given a measurement sequence and HMM parameter estimates, the most likely underlying state sequence can be determined and used to infer beat classification. Advantages of this approach include resistance to noise, ability to model low-amplitude waveforms such as the P wave, and availability of an algorithm for automatically estimating model parameters from training data. We have applied the HMM approach to QRS complex detection and to arrhythmia analysis with encouraging results.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/0022-0736(90)90099-n | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".
J Neurosci Methods
January 2025
School of Electrical and Computer Engineering, Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA.
Background: Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss.
New Method: We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities.
Study of dynamic brain function in irritable bowel syndrome via Hidden Markov Modeling.
Front Neurosci
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Background And Purpose: Irritable bowel syndrome (IBS) is a common bowel-brain interaction disorder whose pathogenesis is unclear. Many studies have investigated abnormal changes in brain function in IBS patients. In this study, we analyzed the dynamic changes in brain function in IBS patients using a Hidden Markov Model (HMM).
View Article and Find Full Text PDFWeight trajectories in aging humanized APOE mice with translational validity to human Alzheimer's risk population: A retrospective analysis.
PLoS One
January 2025
Center for Innovation in Brain Science, University of Arizona Health Sciences, Tucson, Arizona, United States of America.
Translational validity of mouse models of Alzheimer's disease (AD) is variable. Because change in weight is a well-documented precursor of AD, we investigated whether diversity of human AD risk weight phenotypes was evident in a longitudinally characterized cohort of 1,196 female and male humanized APOE (hAPOE) mice, monitored up to 28 months of age which is equivalent to 81 human years. Autoregressive Hidden Markov Model (AHMM) incorporating age, sex, and APOE genotype was employed to identify emergent weight trajectories and phenotypes.
View Article and Find Full Text PDFExploring the Structural Diversity and Biotechnological Potential of the Rhodophyte Phycolectome.
Mar Drugs
December 2024
Biotechnology Unit, University of Ribeirão Preto/UNAERP, Ribeirão Preto 14096-900, SP, Brazil.
Lectins are non-covalent glycan-binding proteins found in all living organisms, binding specifically to carbohydrates through glycan-binding domains. Lectins have various biological functions, including cell signaling, molecular recognition, and innate immune responses, which play multiple roles in the physiological and developmental processes of organisms. Moreover, their diversity enables biotechnological exploration as biomarkers, biosensors, drug-delivery platforms, and lead molecules for anticancer, antidiabetic, and antimicrobial drugs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!