Single-lead electrocardiograms (ECG) are increasingly becoming a reliable modality for long term usage and remote monitoring applications. The development of wearable single-lead ECG devices requires understanding of the inherent characteristics of the acquired signals to create ECG centric signal processing and machine learning (ML) algorithms. However, most single-lead ECG wearable signal analysis blindly implements algorithms that were built for clinical quality ECG signals without taking into consideration the acquired signal properties at hand. This approach leads to inappropriate choice of filtering and ML models which in turn create inaccuracies and biased interpretation of the ECG signals. A framework to examine signal characteristics effectively to guide the development of explainable artificial intelligence (xAI) for newborn ECG monitoring applications is proposed. The 3-minute 3D printed dry electrode newborn ECG signals (n = 19) and their inherent characteristics are examined under different signal properties paradigms. The signals undergo linearity, stationarity, and Gaussian distribution testing. Signal linearity behavior is examined under non-linear autoregression model conditions while stationarity is examined using Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and augmented Dickey–Fuller (ADF) tests. Gaussian behavior is examined using Anderson-Darling (AD) and histogram analysis. Furthermore, the signals undergo cubic Hermite spline interpolation to show the appropriateness of representing the signals through spline polynomial functions. Across all newborn ECG signals non-linear, non-stationary, and non-Gaussian behaviors are observed. The signals can be represented using 4th order polynomials making it suitable for embedded implementation on wearables. Characteristic investigation yields the signal's appropriateness in undergoing a minimum of 4th order filtering techniques and the signal's applicability in non-linear regression models such as support vector machines, decision trees and random forests. By identifying the dry electrode ECG signal characteristics, device-centric algorithmic development is realizable especially for newborn long-term remote ECG monitoring.