The PSO-SVM Recognition Model for Brain Alertness Based on EEG

Abstract

Continuous monitoring of both electrical and mechanical cardiac activity is essential for early detection and management of cardiovascular diseases in real-life environments. This paper presents the design and preliminary evaluation of a chest-worn, Holter-like device that enables 24-hour quad-modal cardiac monitoring by synchronously acquiring electrocardiogram (ECG), phonocardiogram (PCG), seismocardiogram (SCG), and gyrocardiogram (GCG) signals. The main unit is attached to the chest and integrates a heart sound sensor, a 6-axis inertial measurement unit (IMU), data acquisition and storage circuits, and a battery into a single compact housing, while four limb leads (RA, RL, LA, LL) are extended from the device to record ECG. All cardiac signals are sampled at 10 kHz under a shared hardware clock, ensuring absolute temporal synchronization across modalities. Building on the IMU, SCG (chest wall micro-acceleration) and GCG (chest wall micro-rotation) are treated not only as auxiliary motion references, but also as cardio-mechanical signals that are jointly analyzed with ECG. A cross-modal motion artifact suppression framework is proposed, in which ECG, SCG, and GCG mutually constrain each other: motion-dominated components are identified via their inconsistent morphology across modalities, while cardiac components exhibit stable beat-synchronous patterns. The denoised ECG then serves as a temporal reference to perform ECG-guided heart sound segmentation on the PCG, enabling robust extraction of the first to fourth heart sounds (S1–S4). A custom desktop software platform supports synchronized visualization, beat-level quality assessment, and batch analysis of 24-hour recordings. Preliminary tests on healthy subjects during daily activities (resting, walking, posture changes) show that the proposed quad-modal system effectively reduces motion-induced artifacts, improves the morphological consistency of ECG, SCG, and GCG, and achieves reliable multi–heart sound segmentation under ambulatory conditions.The chest-worn, integrated design and cross-modal processing pipeline demonstrate strong potential as a user-friendly and low-cost solution for continuous, multi-dimensional cardiovascular monitoring in clinical and home settings.

Keywords: Alertness, Feature Analysis, EEG, PSO-SVM, Recognitionl

DOI: 10.54941/ahfe1007403