GastroDigitalShirt: A smart shirt for digestion acoustics monitoring - Baronetto, Annalisa; Graf, Luisa; Fischer, Sören; Neurath, Markus; Amft, Oliver (Proceedings - International Symposium on Wearable Computers, ISWC, 2020)
We present the GastroDigitalShirt, a smart T-Shirt for capturing abdominal sounds produced during digestion. The garment prototype embeds an array of eight miniaturised microphones connected to a low-power wearable computer and is designed for long-term recording. We present the microphone integration and shirt wiring layout. With the GastroDigitalShirt we monitored the different digestion phases over six hours in four healthy participants with no prior gastro-intestinal diseases. The collected data were annotated by two independent raters to mark Bowel Sounds (BS) instances. The interrater agreement was substantial, with Cohen's Kappa of 0.7, confirming a consistent labeling approach. Overall 3046 BS instances were individually annotated. The extracted BS were structured by Hierarchical Agglomerative Clustering. The analysis highlighted the presence of 4 BS types. The results show that our prototype can capture the main BS types reported in literature.
CRIS-Team Scopus Importer:2020-11-13
Keywords: bowel sounds; smart garments; wearable sensors

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study. - Baronetto, Annalisa; Graf, Luisa; Fischer, Sarah; Neurath, Markus; Amft, Oliver (JMIR AI, 2024)
BACKGROUND: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.
EVALuna2:590878

Segment-Based Spotting of Bowel Sounds Using Pretrained Models in Continuous Data Streams - Baronetto, Annalisa; Graf, Luisa; Fischer, Sarah; Neurath, Markus; Amft, Oliver (IEEE journal of biomedical and health informatics, 2023)
We analyse pretrained and non-pretrained deep neural models to detect 10-seconds Bowel Sounds (BS) audio segments in continuous audio data streams. The models include MobileNet, EfficientNet, and Distilled Transformer architectures. Models were initially trained on AudioSet and then transferred and evaluated on 84 hours of labelled audio data of eighteen healthy participants. Evaluation data was recorded in a semi-naturalistic daytime setting including movement and background noise using a smart shirt with embedded microphones. The collected dataset was annotated for individual BS events by two independent raters with substantial agreement (Cohen's Kappa κ =0.74). Leave-One-Participant-Out cross-validation for detecting 10-second BS audio segments, i.e. segment-based BS spotting, yielded a best F1 score of 73% and 67%, with and without transfer learning respectively. The best model for segment-based BS spotting was EfficientNet-B2 with an attention module. Our results show that pretrained models could improve F1 score up to 26%, in particular, increasing robustness against background noise. Our segment-based BS spotting approach reduces the amount of audio data to be reviewed by experts from 84 h to 11 h, thus by 87%.
CRIS-Team Scopus Importer:2023-06-02
Keywords: audio tagging; Bowel sound detection; Convolution; Data models; deep learning; Feature extraction; Neural networks; Recording; Training; transfer learning; Transfer learning