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ISSN 0928-7329 (P)
Impact Factor 2023: 1.6
Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured.
The following types of contributions and areas are considered:
1. Original articles:
Technology development in medicine: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine.
Significance of medical technology and informatics for healthcare: The appropriateness, efficacy and usefulness deriving from the application of engineering methods, devices and informatics in medicine and with respect to public health are discussed.
2. Technical notes:
Short communications on novel technical developments with relevance for clinical medicine.
3. Reviews and tutorials (upon invitation only):
Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented.
4. Minisymposia (upon invitation only):
Under the leadership of a Special Editor, controversial issues relating to healthcare are highlighted and discussed by various authors.
Abstract: This paper describes a real time system for the analysis of pulmonary sounds. The system performs various types of time-domain and spectrographic analysis. It is able to display time-domain waveforms obtained from microphones detecting lung sounds, their power spectra and a real-time linear prediction model instantaneously for the immediate identification of interesting features. Details of the system are presented with examples of clinical research carried out using spectrographic analysis.
Abstract: A versatile PC-based lung sound analyzer has been developed for short-term recording and analysis of respiratory sounds in research and clinical applications. The system consists of two sound sensors, a flow sensor, a filtering signal amplifier and a PC with a data acquisition card and software for measurement and analysis of the sounds. The analyses include phonopneumography, time expanded waveform analysis, spectral analysis with time averaged Fast Fourier Transform, frequency analysis in time domain (sonogram), and automatic detection and waveform analysis of crackles. Short-term repeatability of spectral parameters of tracheal and lung sounds was studied in 10 healthy subjects.…The coefficients of variation (CoV) of the averaged quartile frequencies (F 25 , F 50 and F 75 ) of lung sounds during flow-controlled tidal breathing were 3.7, 4.0 and 8.9% in expiration and 2.7, 3.5 and 4.5% in inspiration, respectively. CoVs of the averaged F 25 , F 50 and F 75 of expiratory tracheal sounds were 6.9, 3.0 and 2.4%, and those of inspiratory tracheal sounds 6.3, 2.6 and 3.3%, respectively. Examples of lung sound analysis of samples containing adventitious sounds such as crackles and wheezes are presented. The results indicate that the median frequency has the best repeatability of quartile frequencies of breath sounds and they suggest that the variations of those parameters are low enough for diagnostic purposes. The results also suggest that the analyzer can be a useful new tool for pulmonary research in the fields of physiological and clinical short-term studies of respiratory sounds.
Abstract: Inverse filtering is a digital signal processing technique which may be applied to speech-like sounds to remove resonances introduced by upper airway cavities to leave a residual signal which is, in principle, spectrally flat and strongly related to the excitation source. The filter parameters, normally computed by a form of linear prediction analysis, are indicative of the frequencies and bandwidths of the resonances. This paper briefly outlines the principle of inverse filtering and describes two applications in the study of upper airway sounds for diagnostic purposes. The first application is concerned with the non-invasive measurement of variations in upper airway…dimensions which occur with changes in posture. Results show that differences in the resonance frequencies caused by changes in posture can be measured, these being of the order of about 10% in normals. The measurement of such changes is known to be useful in the assessment of patients with sleep apnoea. The second application concerns the evaluation of vocal tract abnormalities resulting from infection in the larynx. Parameters derived from the residual are believed to be indicative of the existence and severity of a hoarse voice. Results have been obtained which support this theory.
Abstract: A new automatic wheeze detection method which is based on image processing techniques applied to the sonagram was developed here. In the calculation of the sonagram, autoregressive and FFT spectrum estimation methods were compared. The method was validated in four wheezing asthmatic patients by a pulmonary physician. Nine out of ten wheezes longer than 250 ms were detected. Very short wheezes were not detected. The false positive amount of wheezing in control subjects was only about 1%. The method extracts also information about the frequency, duration, flow and volume associated with the wheezes.
Abstract: Wheezes are abnormal sounds which are known to be relevant to Chronic Obstructive Pulmonary Diseases (COPD). The analysis of such signals is especially useful in patient monitoring or pharmacology. Respiratory sounds are dependent on the flow and the volume. Furthermore, they can be the result of a complex mixture of events. The analysis of lung sounds can be greatly improved with time-frequency techniques because these methods highlight the evolution of the spectra of events. In this paper, we present the application of the Adaptive Local Trigonometric Decomposition (ALTD) to lung sound analysis. This analysis provides an optimal representation of the…signal in the time-frequency domain with a lattice which is adapted in time. In our work, the parameterization of the ALTD is studied for the detection of wheezing phenomena.
Abstract: Respiratory sounds are composed of various events: normal and so-called adventitious sounds. These phenomena present a wide range of characteristics which make difficult their analysis with a single technique. Adapted time-frequency and time-scale techniques allow to fit best, under constraints, the accuracy of analysis of a time segmentation and, by the way, make feasible the study of complex signals. We present here new approaches based only on the wavelet packet decomposition to segment respiratory sounds.
Abstract: In this paper, a wavelet packet-based method is used for detection of abnormal respiratory sounds. The sound signal is divided into segments, and a feature vector for classification is formed using the results of the search for the best wavelet packet decomposition. The segments are classified as containing crackles, wheezes or normal lung sounds, using Learning Vector Quantization. The method is tested using a small set of real patient data which was also analysed by an expert observer. The preliminary results are promising, although not yet good enough for clinical use.
Abstract: In this paper, an automatic method to detect and analyze crackles in digitised respiratory sounds is presented. The method is based on two steps: (1) a threshold (T ) value is applied to the first derivative absolute value (FDAV) of lung sound to locate the “zone of interest” and (2) in this zone a crackle is detected if certain conditions are verified. The first derivative (FD) is evaluated by means of a derivative-smoothing filter, preserving areas under the spectral lines of the signal (moment zero), its mean position in time (first moment) and its spectral line width (second moment). The…conditions to verify step 2 concern the following: the number and height of the peaks of FDAV and their distances from the starting point of the crackle, within a temporal window T W . This method shows good performances as an automatic detector (sensitivity 84% and specificity 89%), and is specifically designed to find the starting point of the crackle.
Abstract: A new method to represent and evaluate crackles on the flow-volume plane is described. Characteristic crackle patterns were found in patients with pneumonia, bronchiectasis, chronic obstructive pulmonary disease, heart failure and cryptogenic fibrosing alveolitis. In addition to visual assessment, simple statistical parameters were used to describe the observed pathological phenomena.