Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies.
Electrocardiography (ECG) data are multidimensional temporal data with ubiquitous applications in the clinic. Conventionally, these data are presented visually. It is presently unclear to what degree data sonification (auditory display), can enable the detection of clinically relevant cardiac pathologies in ECG data. In this study, we introduce a method for polyphonic sonification of ECG data, whereby different ECG channels are simultaneously represented by sound of different pitch. We retrospectively applied this method to 12 samples from a publicly available ECG database. We and colleagues from our professional environment then analyzed these data in a blinded way. Based on these analyses, we found that the sonification technique can be intuitively understood after a short training session. On average, the correct classification rate for observers trained in cardiology was 78%, compared to 68% and 50% for observers not trained in cardiology or not trained in medicine at all, respectively. These values compare to an expected random guessing performance of 25%. Strikingly, 27% of all observers had a classification accuracy over 90%, indicating that sonification can be very successfully used by talented individuals. These findings can serve as a baseline for potential clinical applications of ECG sonification.
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.|Department of Medical Oncology and Internal Medicine VI, National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg University, Heidelberg, Germany.
Jakob Nikolas
JN
Kather
Ambient Intelligence Group, Center of Excellence in Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany.
Thomas
T
Hermann
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Yannick
Y
Bukschat
Klinik III für Innere Medizin, Herzzentrum der Universität zu Köln, Cologne, Germany.
Tilmann
T
Kramer
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Lothar R
LR
Schad
Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Frank Gerrit
FG
Zöllner
D001281:Atrial Fibrillation / Q000000981:diagnostic imaging* / Q000503:physiopathology; D016208:Databases, Factual; D004562:Electrocardiography / Q000295:instrumentation / Q000379:methods*; D006321:Heart / Q000000981:diagnostic imaging* / Q000503:physiopathology; D006801:Humans; D046709:Pattern Recognition, Physiological / Q000502:physiology*; D010364:Pattern Recognition, Visual / Q000502:physiology; D012189:Retrospective Studies; D000072657:ST Elevation Myocardial Infarction / Q000000981:diagnostic imaging* / Q000503:physiopathology; D013016:Sound; D018879:Ventricular Premature Complexes / Q000000981:diagnostic imaging* / Q000503:physiopathology