Wearable devices are on the rise. Smart watches and phones, fitness trackers or smart textiles now provide unprecedented access to our own personal data. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor the heart's and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heartrate when they are attached to the chest. They can also help filter out some noise in ECG signal from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen), with a standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is done with eight volunteers, writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685x10$^{-3}$ comparing the DigiPen's computed $Δ$t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates from the pen accurately correlate with the reference ECG signals.