Real-time anomaly detection in time series data is crucial for various applications and requires several parameters to be defined prior to the proper analyses. The window size is one of those parameters which indicates the batch data size that is analyzed in each iteration. A recently published Pitchy Anomaly Detection (PAD) algorithm, which combines spectral and time-domain analysis, is used to investigate the effects of window size on anomaly detection performance and computational complexities. The study uses two pre-annotated ECG datasets, and a synthetically generated dataset under different data size conditions to perform the experiments. Results show that smaller window sizes generally lead to improved accuracy and F1-scores, especially in detecting ventricular anomalies. However, excessively small windows can lead to a loss of contextual information and negatively impact overall performance.