Pneumonia causes the deaths of over a million people worldwide each year, with most occurring in countries with limited access to expensive but effective diagnostic methods, e.g., chest X-rays. Physical examination, the other major established method of diagnosis, suffers from several drawbacks, most notably low accuracy and high interobserver error. We sought to address this diagnostic gap by developing a proof-of-concept non-invasive device to identify the accumulation of fluid in the lungs (consolidation) characteristic of pneumonia. This device, named Tabla after the percussive instrument of the same name, utilizes the technique of auscultatory percussion; a percussive input sound is sent through the chest and recorded with a digital stethoscope for analysis. Tabla analyzes differences in sound transmission through the chest at audible frequencies as a marker for lung consolidation. This paper presents preliminary data from five pneumonia patients and eight healthy subjects. We demonstrate 92.3% accuracy in distinguishing between healthy subjects and patients with pneumonia after data analysis with a K-nearest neighbors algorithm. This prototype device is low cost and simple to implement and may offer a rapid and inexpensive method for pneumonia diagnosis appropriate for general use and in areas with limited medical infrastructure.

/pdf/tabla-a-proof-of-concept-auscultatory-percussion-device-for-v933pw5at6.pdf

Tabla: A Proof-of-Concept Auscultatory Percussion Device for Low-Cost Pneumonia Detection.

Matrix Pencils facilitate the study of differential equations resulting from oscillating systems. Certain problems in linear ordinary differential equations, such as speech processing, can be represented as the problem of finding a canonical pencil strictly equivalent to a given pencil. It was originally applied by the radar community, to phased array radar. In this type of application the problem of signal directional finding is solved and nulls are formed in the direction of noise. 
The Matrix Pencil (MP) algorithm is a direct data approach, which depends on only the data and not statistical information. This approach has many benefits over a statistical approach. One benefit allows the user to approximate the error of the reconstructed signal without reconstructing the signal. Second, it takes less time and less computational power to execute the algorithm. Third, the matrix pencil approach has a lower variance of the estimates of the parameters of interest than a statistical approach such as traditional Linear Prediction Coding (LPC). 
Speech processing has many applications, which directly assist in the advancement of technology. These technologies utilize speech tools that include, but are not limited to speech compression, speech enhancement, speech recovery, pitch estimation, and cochannel interference reduction. However, the speech processing community has not grasped the power of the MP algorithm, which will likely make a significant leap forward in improving these speech processing tools. Similar to 15 years ago, the MP algorithm will do for the speech community, what it did for the radar community.

The applications of the matrix pencil to speech processing

The method of generalized eigenvector decomposition (GEVD) is proposed for extracting the poles of a radar target by using the autocorrelation of target late-time impulse responses in the paper The additive noise in the signal is considered in the method It can be used to evaluate the poles when only the amplitude data of multiple frequency returns is available The simulation results in the paper illustrate that the new method has advantages over the generalized pencil of function (GFOF) method in the noise sensitivity and the computation >

https://ieeexplore.ieee.org/iel2/1063/7761/00328059.pdf

Generalized eigenvector decomposition method for pole extraction and GPOF method

Josef Leopold Auenbrugger (1722 - 1809): father of percussion.

An Improved Method of Lung Percussion

Used for centuries in the clinical practice, audible percussion is a method of eliciting sounds by areas of the human body either by finger tips or by a percussion hammer. Despite its advantages, pulmonary diagnostics by percussion is still highly subjective, depends on the physician's skills, and requires quiet surroundings. Automation of this well-established technique could help amplify its existing merits while removing the above drawbacks. In this study, an attempt is made to automatically decompose clinical percussion signals into a sum of Exponentially Damped Sinusoids (EDS) using Matrix Pencil Method, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. It is found that some EDS represent transient oscillation modes of the thorax/abdomen excited by the percussion event, while others are associated with the noise. It is demonstrated that relatively few EDS are usually enough to accurately reconstruct the original signal. It is shown that combining the frequency and damping parameters of these most significant EDS allows for efficient classification of percussion signals into the two main types historically known as "resonant" and "tympanic". This classification ability can provide a basis for the automated objective diagnostics of various pulmonary pathologies including pneumothorax.

/pdf/automated-classification-of-medical-percussion-signals-for-db8hbg6u38.pdf

Automated Classification of Medical Percussion Signals for the Diagnosis of Pulmonary Injuries

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Automated Classification of Medical Percussion Signals for the Diagnosis of Pulmonary Injuries