Estimating the parameters of audible clinical percussion signals by fitting exponentially damped harmonics

A medical percussion instrument using a wavelet-based method for archivable output and automatic classification

There is no standard instrument for carrying out medical percussion even though the procedure has been in continuous use since 1761. This study developed one such instrument. It generates medical percussion sounds in a reproducible manner and accurately classifies them into one of three classes. Percussion signals were generated using a push-pull solenoid plessor applying mechanical impulses through a polyvinyl chloride plessimeter. Signals were acquired using a National Instruments USB 6251 data acquisition card at a rate of 8.192 kHz through an air-coupled omnidirectional electret microphone located 60 mm from the impact site. Signal acquisition, processing, and classification were controlled by an NVIDIA Jetson TX2 computational device. A complex Morlet wavelet was selected as the base wavelet for the wavelet decomposition using the maximum wavelet energy method. It was also used to generate a scalogram suitable for manual or automatic classification. Automatic classification was achieved using a MobileNetv2 convolutional neural network with 17 inverted residual layers on the basis of 224 × 224 x 1 images generated by downsampling each scalogram. Testing was carried out using five human subjects with impulses applied at three thoracic sites each to elicit dull, resonant, and tympanic signals respectively. Classifier training utilized the Adam algorithm with a learning rate of 0.001, and first and second moments of 0.9 and 0.999 respectively for 100 epochs, with early stopping. Mean subject-specific validation and test accuracies of 95.9±1.6% and 93.8±2.3% respectively were obtained, along with cross-subject validation and test accuracies of 94.9% and 94.0% respectively. These results compare very favorably with previously-reported systems for automatic generation and classification of percussion sounds.

Room acoustic modal analysis using Bayesian inference

Strong modal behavior can produce undesirable acoustical effects, particularly in recording studios and other small rooms. Although closed-form solutions exist to predict modes in rectangular rooms with parallel walls, such solutions are typically not available for rooms with even modest geometrical complexity. This work explores a method to identify multiple decaying modes in experimentally measured impulse responses from existing spaces. The method adopts a Bayesian approach working in the time domain to identify numerous decaying modes in an impulse response. Bayesian analysis provides a unified framework for two levels of inference: model selection and parameter estimation. In this context model selection determines the number of modes present in an impulse response, while parameter estimation determines the relevant parameters (e.g., decay time and frequency) of each mode. The Bayesian analysis in this work is implemented using an approximate numerical technique called nested sampling. Experimental measurements are performed in a test chamber in two different configurations. Experimentally measured results are compared with simulated values from the Bayesian analyses along with other, more classical calculations. Discussion of the results and the applicability of the method is provided.

Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method

This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals. The MPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.