# Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound MultiLabelling

arXiv: https://arxiv.org/abs/2204.07420 · HTML: https://mlsanity.com/paper/2204.07420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification](https://mlsanity.com/paper/2108.00813/) (2021)
- [Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection](https://mlsanity.com/paper/1801.08322/) (2018)
- [Short-segment heart sound classification using an ensemble of deep convolutional neural networks](https://mlsanity.com/paper/1810.11573/) (2018)
- [Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks](https://mlsanity.com/paper/1810.04121/) (2018)
- [Bayesian LSTMs in medicine](https://mlsanity.com/paper/1706.01242/) (2017)

## Abstract (arXiv)

Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels from different label groups, including murmur's timing, pitch, grading, quality, and shape. Our experiment results show that the proposed method has achieved outstanding performance on the holdout data for the multi-labelling task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level, and an overall accuracy=0.969 at the patient's recording level.