Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies

Kather JN, Hermann T, Bukschat Y, Kramer T, Schad LR, Zöllner FG. Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies. Scientific Reports. 2017;7(1): 44549.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. 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

Supplementary Material for "Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies"

Kather JN, Hermann T, Bukschat Y, Kramer T, Schad LR, Zöllner FG. Supplementary Material for "Polyphonic sonification of electrocardiography signals for diagnosis of cardiac pathologies". Bielefeld University; 2017.#### S1: Normal ECG sample <img src="https://pub.uni-bielefeld.de/download/2908653/2908701" width="40%" height="40%" style="float:right;" > Description: This supplementary material contains ECG data sonification of a healthy control. * [s0306lrem.wav](https://pub.uni-bielefeld.de/download/2908653/2908702) contains sound data for a six-channel data set, 10 seconds * [s0306lrem.png](https://pub.uni-bielefeld.de/download/2908653/2908701) contains the corresponding visual data, 10 seconds, sampling rate of 1000 Hz #### S2: Incremental signal Description: This supplementary material contains an incremental number of channels from a pathological (STEMI) ECG data sonification <img src="https://pub.uni-bielefeld.de/download/2908653/2908709" width="40%" height="40%" style="float:right;" > - [incremental_01.wav](https://pub.uni-bielefeld.de/download/2908653/2908703) only channel III (lowest), 10 seconds - [incremental_02.wav](https://pub.uni-bielefeld.de/download/2908653/2908704) channel III and aVF, 10 seconds - [incremental_03.wav](https://pub.uni-bielefeld.de/download/2908653/2908705) channel III and aVF and II, 10 seconds - [incremental_04.wav](https://pub.uni-bielefeld.de/download/2908653/2908706) channel III and aVF and II and -aVR, 10 seconds - [incremental_05.wav](https://pub.uni-bielefeld.de/download/2908653/2908707) channels III through I, 10 seconds - [incremental_06.wav](https://pub.uni-bielefeld.de/download/2908653/2908708) all channels, 10 seconds - [incremental_signal.png](https://pub.uni-bielefeld.de/download/2908653/2908709) shows the corresponding visual data, 10 seconds, sampling rate of 257 Hz #### S3: Pathological ECG samples Description: This supplementary material contains sonified ECG data of four pathological samples, corresponding to Figure 2 in the main manuscript. All samples are 10 seconds in length (original sound file in sub-folder “original”, amplified sound file in sub-folder “amplified”). - [Sample_I04m_STEMI.wav](https://pub.uni-bielefeld.de/download/2908653/2908710) ST-elevation myocardial infarction - [Sample_I37m_PVC.wav](https://pub.uni-bielefeld.de/download/2908653/2908711) Premature ventricular contraction - [Sample_I50m_AF.wav](https://pub.uni-bielefeld.de/download/2908653/2908712) Atrial fibrillation - [Sample_I51m_Bigeminy.wav](https://pub.uni-bielefeld.de/download/2908653/2908713) Bigeminy #### S4: Flowchart of the algorithm File: [S4_sonification_procedure_schematic.pdf](https://pub.uni-bielefeld.de/download/2908653/2908714) Description: This PDF is a detailed flowchart of the algorithm including all relevant parameters. This can be used to implement our proposed method in any programming language. #### S5: Observer performance during data analysis File: [S5_performance_data.xls](https://pub.uni-bielefeld.de/download/2908653/2908715) Description: The spreadsheet contains all results of the data analysis by 22 blinded observers in three groups. S1 to S12 refer to the ECG samples, the number in each cell in these columns shows the classification by the observer. Correct classifications are shown in green, errors are shown in red. “Instrument” denotes whether the observer had been actively playing an instrument for three or more years at any time during their life. #### S6 Source code of Polyphonic ECG Sonification (in SuperCollider3) File: [S6_Polyphonic-ECG-Sonification.zip](https://pub.uni-bielefeld.de/download/2908653/2908761) Description: The zipped folder contains the Supercollider3 source code, two data files in csv format and the resulting sonifications for Polyphonic ECG Sonification. Instructions for how to render the sonifications are given in the source code header. #### S7 Source code of Polyphonic ECG Sonification (Matlab) File: [S7_Polyphonic-ECG-Sonification-matlab.zip](https://pub.uni-bielefeld.de/download/2908653/2908760) Description: This file contains the source code in matlab which was also used for the rendering of sonifications in the study

Comparison of synthetic dataset generation methods for medical intervention rooms using medical clothing detection as an example

Abstract Purpose The availability of real data from areas with high privacy requirements, such as the medical intervention space is low and the acquisition complex in terms of data protection. To enable research for assistance systems in the medical intervention room, new methods for data generation for these areas must be researched. Therefore, this work presents a way to create a synthetic dataset for the medical context, using medical clothing object detection as an example. The goal is to close the reality gap between the synthetic and real data. Methods Methods of 3D-scanned clothing and designed clothing are compared in a Domain-Randomization and Structured-Domain-Randomization scenario using two different rendering engines. Additionally, a Mixed-Reality dataset in front of a greenscreen and a target domain dataset were used while the latter is used to evaluate the different datasets. The experiments conducted are to show whether scanned clothing or designed clothing produce better results in Domain Randomization and Structured Domain Randomization. Likewise, a baseline will be generated using the mixed reality data. In a further experiment it is investigated whether the combination of real, synthetic and mixed reality image data improves the accuracy compared to real data only. Results Our experiments show, that Structured-Domain-Randomization of designed clothing together with Mixed-Reality data provide a baseline achieving 72.0% mAP on the test dataset of the clinical target domain. When additionally using 15% (99 images) of available target domain train data, the gap towards 100% (660 images) target domain train data could be nearly closed 80.05% mAP (81.95% mAP). Finally, we show that when additionally using 100% target domain train data the accuracy could be increased to 83.35% mAP. Conclusion In conclusion, it can be stated that the presented modeling of health professionals is a promising methodology to address the challenge of missing datasets from medical intervention rooms. We will further investigate it on various tasks, like assistance systems, in the medical domain

Color-coded visualization of mpMRI data - visualization results

<p>This repository contains raw data from the paper: "Color-coded visualization of magnetic resonance imaging multiparametric maps", published in "Scientific Reports", 2016. For details on the data, please refer to the paper or contact JN Kather directly (http://orcid.org/0000-0002-3730-5348).</p> <p>All image files are provided as DICOM files (fully anonymized). Please note that not all DICOM viewers are capable of displaying RGB images. If the images in the "Trivariate" channel are not shown in color, please try the OsiriX free DICOM viewer (http://www.osirix-viewer.com).</p