Characteristics of Electro-Mechanical Signals Originating from Striated Muscle Contractions with Applications to Muscular Dystrophy and Cardiac Conditions

Author

• Sherif Ahdy Alsaeed Farahat, University of Central FloridaFollow

ORCID

0000-0002-7935-1465

Keywords

SCG, EMG, MMG, Variability, Clustering, DMD

Abstract

Non-invasive monitoring of heart failure and skeletal muscle dystrophy requires understanding the characteristics of the electrical and mechanical signals of these muscles under different conditions. These signals include seismocardiography (SCG), electrocardiography (ECG), electromyography (EMG), mechanomyography (MMG), and muscle force. In the first module of this work, the skeletal muscles of healthy subjects and patients with Duchenne muscular dystrophy (DMD) were investigated non-invasively. The force, surface EMG, and MMG signals were acquired, their signal features were extracted, and the features were compared between healthy and DMD subjects. It was shown that DMD muscles generate lower force and have lower neuromuscular efficiency, slower contraction and relaxation and higher stiffness than healthy muscles. The second module focused on SCG signal characteristics. This module investigated the effect of respiration-related parameters on SCG variability and spectral energy, the spatial distribution of SCG clustering and the effect of the initial condition on SCG clustering using unsupervised machine learning. Results showed that as the airway (gauge) pressure deviated from zero during breath hold, SCG variability increased. The subaudible to audible spectral energy ratio increased as the airway pressure decreased. SCG morphology was found to vary during the respiratory cycle and can form two clusters. To determine the two clusters, an initial condition is needed for the machine learning algorithm. Results showed that the flow rate based initial condition outperformed the lung volume based initial condition in terms of the clustering accuracy, spatial consistency, intra-cluster variability reduction, and inter-subject agreement. This work suggested a possible time-efficient procedure to obtain preferred clustering solutions based on a subset of the segmented SCG beats for initial conditions. These solutions include the solution that has the maximum difference between the inter-cluster and the intra-cluster variabilities and the highest purity solution.

Completion Date

2025

Semester

Fall

Committee Chair

Mansy, Hansen

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Mechanical and Aerospace Engineering

Format

PDF

Release Date

12-15-2026

Document Type

Dissertation

Campus Location

Orlando (Main) Campus

Subjects

Electromyography--Data processing; Muscular dystrophy--Research; Muscles--Mechanical properties; Muscle contraction--Measurement; Electrocardiography--Mathematical models

STARS Citation

Farahat, Sherif Ahdy Alsaeed, "Characteristics of Electro-Mechanical Signals Originating from Striated Muscle Contractions with Applications to Muscular Dystrophy and Cardiac Conditions" (2025). Graduate Thesis and Dissertation post-2024. 529.
https://stars.library.ucf.edu/etd2024/529