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3. Methodology
The machine-learning stacking scheme used in this study is presented in Fig. 1. The
following subsections describe in detail the key steps of the pipeline.
3.1. Filtering and segmentation
According to Choi et al [2], the majority of cardiovascular sounds are most likely to
occur in the frequency range below 1 kHz, thus, we filtered the raw audio files using
low-pass Butterworth filter with a threshold of 1 kHz. For segmentation of the filtered
audio signal, we used a sliding window of 1 s with an overlap of 0.5 s. This provides
audio segments with a duration of 1 s. The size of the window was chosen with the
reasoning that for an average heart rate of 60 beats per minute, the 1 s window should
contain at least one heartbeat, thus containing all the relevant information about the CHF-
related sounds.
1. RECORDING (RAW)
2. FILTERING
3. SEGMENTATION
4. FEATURE EXTRACTION
5. FEATURE SELECTION
6. SEGMENT CLASSIFIER
7. SEGMENT AGGREGATION
8. RECORDING CLASSIFIER
EVALUATION
Figure 1. Machine-learning stacking scheme used for the analysis performed in this study.
3.2. Feature extraction
3.2.1. Audio features
We used the OpenSMILE tool [3] for large feature-space extraction. The tool was
originally created for acoustic emotion recognition in 2009 but later expanded to more
general uses. For example, in addition to affect recognition, it is widely used for music
information retrieval (e.g., chord labelling, beat tracking, and onset detection). We
extracted 1941 audio features, including statistical features (e.g., variance, skewness,
kurtosis), energy-based features (e.g., energy in bands from 250 to 1 kHz), frequency-
based features (25 %, 50 %, 75 %, and 90 % spectral roll-off points) and voicing-related
features (e.g., jitter, shimmer, Harmonics-to-Noise Ratio). The complete list of features
is described by Schuller et al. [4].
M.Gjoreski etal.
/TowardEarlyDetectionandMonitoringofChronicHeartFailure338
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Titel
- Intelligent Environments 2019
- Untertitel
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Autoren
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Verlag
- IOS Press BV
- Datum
- 2019
- Sprache
- deutsch
- Lizenz
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Abmessungen
- 16.0 x 24.0 cm
- Seiten
- 416
- Kategorie
- Tagungsbände