Identification of sudden cardiac death from human blood using ATR-FTIR spectroscopy and machine learning

Abstract

Objective: The aim of this study is to identify a rapid, sensitive, and non-destructive auxiliary approach for postmortem diagnosis of SCD, addressing the challenges faced in forensic practice.

Methods: ATR-FTIR spectroscopy was employed to collect spectral features of blood samples from different cases, combined with pathological changes. Mixed datasets were analyzed using ANN, KNN, RF, and SVM algorithms. Evaluation metrics such as accuracy, precision, recall, F1-score and confusion matrix were used to select the optimal algorithm and construct the postmortem diagnosis model for SCD.

Results: A total of 77 cases were collected, including 43 cases in the SCD group and 34 cases in the non-SCD group. A total of 693 spectrogram were obtained. Compared to other algorithms, the SVM algorithm demonstrated the highest accuracy, reaching 95.83% based on spectral biomarkers. Furthermore, by combing spectral biomarkers with age, gender, and cardiac histopathological changes, the accuracy of the SVM model could get 100%.

Conclusion: Integrating artificial intelligence technology, pathology, and physical chemistry analysis of blood components can serve as an effective auxiliary method for postmortem diagnosis of SCD.

Keywords: ATR-FTIR spectroscopy; Machine learning; Postmortem diagnosis; SCD.