A new collective anomaly detection approach using pitch frequency and dissimilarity: Pitchy anomaly detection (PAD)

Anomaly detection in time series is an important process that can aid in both preprocessing and postprocessing, particularly in biomedical data modalities where anomalies often signify the presence of disorders that require identification. However, quasi-periodic data behavior frequently poses a challenge in detecting collective anomalies, especially when they fall within the normal data range. This difficulty makes detecting collective anomalies particularly challenging. This paper proposes a new method for detecting collective anomalies using pitch frequency information from the frequency domain and dissimilarity metric scores in a sliding window approach. The proposed method called pitchy anomaly detection (PAD) is expected to be effective in the analysis of quasi-periodic time series data. To evaluate the effectiveness of the PAD approach, it is compared to three other benchmark time series anomaly detection methods. Due to the particular interest in quasi -periodic data modalities, the comparative analysis is conducted under five different anomalous conditions on anomaly-added benchmark electrocardiography (ECG) datasets. The results show that the PAD approach yields promising classification performance results for detecting collective anomalies.