Clustering
Studies in this Category
Postural and longitudinal variability in seismocardiographic signals
This study shows that SCG signals, which measure heart vibrations, change with posture but remain stable over time, making them promising for long-term heart monitoring.
Effect of the Airway Pressure on the Frequency Domain of Seismocardiographic Signal
This study shows how changes in breathing pressure affect heart vibrations, which could help monitor heart muscle health in the future.
Effect of Normal Breathing and Breath Holding on Seismocardiographic Signals and Heart Rate
This study shows that holding your breath can make heart vibration signals more consistent, which could help improve heart health monitoring techniques.
Smart Seismocardiography: A Machine Learning Approach for Automatic Data Processing
This research shows how a low-cost sensor and machine learning can track heart vibrations to monitor cardiac health, paving the way for affordable wearable devices.
A Machine Learning Approach to Assess the Separation of Seismocardiographic Signals by Respiration
This study shows that machine learning can classify heart vibrations based on breathing patterns, with lung volume proving to be a better grouping method than respiratory phases for reducing signal variability.
BCG Artifact Removal Using Improved Independent Component Analysis Approach
This research presents a new method to clean heart vibration signals (BCG) by removing noise caused by movement, using advanced mathematical techniques like ICA and clustering. It improves signal quality for better health monitoring.