There is a current trend towards the use of wearable biomedical devices for the purpose of recording various biosignals, such as electrocardiograms (ECG). Wearable devices have different issues and challenges compared to nonwearable ones, including motion artifacts and contact characteristics related to body-conforming materials. Due to this susceptibility to noise and artifacts, signals acquired from wearable devices may lead to incorrect interpretations, including false alarms and misdiagnoses. This research addresses two challenges of wearable devices. First, it investigates the effect of applied pressure on biopotential electrodes that are in contact with the skin. The pressure affects skin–electrode impedance, which impacts the quality of the acquired signal. We propose a setup for measuring skin–electrode impedance during a sequence of applied calibrated pressures. The Cole–Cole impedance model is utilized to model the skin–electrode interface. Model parameters are extracted and compared in each state of measurement with respect to the amount of pressure applied. The results indicate that there is a large change in the magnitude of skin–electrode impedance when the pressure is applied for the first time, and slight changes in impedance are observed with successive application and release of pressure. Second, this research assesses the quality of ECG signals to reduce issues related to poor-quality signals, such as false alarms. We design an algorithm based on Deep Belief Networks (DBN) to distinguish clean from contaminated ECGs and validate it by applying real clean ECG signals taken from the MIT-BIH arrhythmia database of Physionet and contaminated signals with motion artifacts at varying signal-to-noise ratios (SNR). The results demonstrate that the algorithm can recognize clean from contaminated signals with an accuracy of 99.5% for signals with an SNR of -10 dB. Once low- and high-quality signals are separated, low-quality signals can undergo additional pre-processing to mitigate the contaminants, or they can simply be discarded. This approach is applied to reduce the false alarms caused by poor-quality ECG signals in atrial fibrillation (AFib) detection algorithms. We propose a signal quality gating system based on DBN and validate it with AFib signals taken from the MIT-BIH Atrial Fibrillation database of Physionet. Without gating, the AFib detection accuracy was 87% for clean ECGs, but it markedly decreased as the SNR decreased, with an accuracy of 58.7% at an SNR of -20 dB. With signal quality gating, the accuracy remained high for clean ECGs (87%) and increased for low SNR signals (81% for an SNR of -20 dB). Furthermore, since the desired level of quality is application dependent, we design a DBN-based algorithm to quantify the quality of ECG signals. Real ECG signals with various types of arrhythmias, contaminated with motion artifacts at several SNR levels, are thereby classified based on their SNRs. The results show that our algorithm can perform a multi-class classification with an accuracy of 99.4% for signals with an SNR of -20 dB and an accuracy of 91.2% for signals with an SNR of 10 dB.