The COVID-19 pandemic revealed the importance of a strong telehealth infrastructure for continuous remote monitoring and diagnosis. This aspect can be expanded to newborns, and the added role of parents to convey their newborns' medical information to pediatricians through telehealth applications. An important factor that physicians rely on in newborn diagnosis and monitoring is heart rate (HR). A wireless hand-held single-lead 3D printed dry electrode electrocardiogram (ECG) monitoring device has been developed and deployed for newborn HR assessment. The electrodes are 3D printed using conductive polylactic acid (PLA) filaments. A small subset of 3-minute newborn ECGs (n=5) with an average gestation period of 38 weeks collected at 500 Hz sampling frequency is used to ensure dry electrode single-lead ECG quality for fast newborn HR detection. To ensure signal reliability, statistical signal quality indexes (SQIs) are computed such as kurtosis, skewness, variance, standard deviation (SD), and signal mobility. Furthermore, HR information is estimated using a modified Pan-Tompkins algorithm, and signal to noise ratio (SNR) in decibels (dB) is computed. Device HR estimation is compared to a clinical-grade HR monitor. Average SQIs showed 0.199, 37.99, and 2.22 for skewness, kurtosis, and mobility respectively. The single-lead ECG device provided an HR detection accuracy of 92.1% with a maximum of 10 seconds of acquisition time from contact to detection. Visually, the QRS complex and T waveforms are obvious in all subjects with a 16.34 dB average SNR, but P-wave is not clear due to high-frequency noise present. It is evident that 3D-printed dry electrodes can be used for neonatal HR monitoring applications where R-peak and RR-interval information can be extracted from the ECG.