Akademik Veri Yönetim Sistemi
Cybernetics and Systems, 2023 (SCI-Expanded)
The proposed study brings a novel classification and detection ability for stochastic environmental conditions by its WSS (Wave-Segment Synthesizing) functionality by tightly adapting a Lightweight CNN with an increase of about 40% accuracy over the literature. This study synthesizes heterogeneously coherent ECG signals and adapts them to the input system of wearable devices. It consists of novel operations which are called Wave Form Geneticization, ECG Segment Shifting, Time Contraction, and Expansion, Magnification, Projection, Permutation, Upscaling, Downscaling, Noising, and Denoising on the signal pattern to ensure a robust anomaly detection mechanism in such a sensor-independent and tempered manner. The proposed WSS Algorithm for heterogeneous harmonization of ECG signals differs from the literature by its robust classification capability of anomalies even in the weakest, loudest, most unstable, and most corrupt data collection environments. It provides a platform-independent mechanism for wearable technologies with low computational capability. Thus, anomaly detection will be more accessible to following the disorders in the circulatory system and making an early diagnosis before death. It mitigates the vulnerabilities of literature models that are affected by the deterioration of environmental factors such as sea level, pressure, optical effects, altitude, and temperature. The applied dynamic algorithm is a candidate to be a pioneer for detecting abnormalities in the heart that closely concerns all body systems in the human body.