Enhancement of bowel sounds by wavelet-based filtering

Hadjileontiadis, Leontios; Liatsos, Christos; Mavrogiannis, Christos; Rokkas, Theodoros; Panas, S.M.

doi:10.1109/10.846681

Cited by 62 publications

(46 citation statements)

References 17 publications

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“…For the last few decades, there have been various comparative studies on the BSs of normal subjects and those of patients with bowel dysfunction, such as irritable bowel syndrome [5,6,8], Crohn's disease [6], diabetes mellitus [7] and obstruction [18], and their results showed significant differences between the features obtained from BS signals according to the pathological condition of bowel motility. Also, several researchers recently have developed the de-noising, segmentation and feature extraction strategies of BS signals based on various signal-processing techniques [9-16,19].…”

Section: Discussionmentioning

confidence: 99%

“…In relation to recording-duration issues of our approach, the BS signals were analyzed based on short-term analysis that deals with small-duration samples of the entire activity in fasted humans, coincided with [5-8,18,19], and all of the features were obtained from impulsive BSs due to the management difficulty of subject's immobilization during the test and huge data. On the contrary, many researchers believe that the period of BS monitoring should be longer over at least 2 hours since lasting/regularly-sustained (RS) BSs monitored during a full migrating motor complex (MMC) cycle are also associated with bowel motility [4,10-16].…”

Section: Discussionmentioning

confidence: 99%

“…Yamaguchi et al [7] showed that the sound index of the gastro-duodenal sound in the diabetes mellitus patients was significantly lower after food intake than in healthy adults. Also, wavelet-based strategies for the signal acquisition, de-noising, automated segmentation, event detection and feature extraction of bowel sounds were proposed [8-11]. Dimoulas et al [12] implemented an autonomous BS monitoring system utilizing wavelet feature extraction and multi-layer perceptrons (MLP) network classifiers for the pattern classification of BS segments.…”

Section: Introductionmentioning

confidence: 99%

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Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

2011

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BackgroundRadiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised.MethodsTwelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN.ResultsAs a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively.ConclusionsThe jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

2011

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“…However, the noise robustness of the WTST-NST filter overcomes most of the stationary background noise usually met in clinical practice, as is thoroughly described in HADJILEONTIADIS et al (2000); thus this limitation will be met only in cases where a high level of non-stationary noise (e.g. speech, patient movement) is present.…”

Section: Discussionmentioning

confidence: 97%

Discrimination analysis of discontinuous breath sounds using higher-order crossings

Hadjileontiadis

2003

Med. Biol. Eng. Comput.

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The paper evaluates the performance of an automatic discrimination analysis (DA) method used to discriminate efficiently the types of discontinuous breath sound (DBS), i.e. fine crackles (FCs), coarse crackles (CCs) and squawks (SQs); this may lead to more accurate characterisation of the pulmonary acoustical changes due to the related pathology. Based on higher-order crossings (HOCs), the proposed method, HOC-DA, captured the differences in the oscillatory patterns of FCs, CCs and SQs, which are only exposed when higher (> 1) crossings are employed. Prior to HOC-DA, wavelet-based de-noising of DBSs was employed to eliminate the effects of the vesicular sound (background noise) from their oscillatory pattern. The HOC-DA was applied to 157 discontinuous breath sounds corresponding to 16 cases included in three lung sound databases. Results showed that the HOC-DA efficiently separated FCs from CCs, SQs from CCs (both with an accuracy of 100%), and SQs from FCs (accuracy of 80%), with the optimum order ranging from 9 to 11. When compared with other classification tools, the HOC-DA resulted in high discrimination accuracy without involving high computational complexity. Owing to its simplicity, it could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

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“…The four independent components are subjected to the modified WTST-NST filtering [Ref], and then the spectrograms are calculated. Note that suitably-sized white noises are superimposed on the data after the modified WTST-NST filtering [6,9] in order to stabilize the next step. Next, template matching of the four spectrograms using the standard bowel sound spectrum [2][3][4][5] is carried out to derive four mutual correlation functions.…”

Section: A Methods Of Measuring and Analyzing Bowel Soundsmentioning

confidence: 99%

Basic study of occurrence frequency of bowel sounds after food ingestion

Sato

Matsuda

Suzuki

et al. 2011

TENCON 2011 - 2011 IEEE Region 10 Conference

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The human bowel produces bowel sounds characteristic of digestive activity. The changes in digestive activity of the bowel are reflected in the frequency and intensity of peristaltic action, and indirectly in variation in bowel sound. Approaching this process in reverse, we detected changes in bowel sound to capture variation in digestive activity of the bowel, aiming to develop a quantitative monitoring technique to trace the cause of the bowel sound variation back to a difference in consumed foods, to compatibility between the food and the individual, or to the health of the individual's digestive organs. In order to collect the basic data for developing such a technique, we carried out two experiments. Experiment 1 was an examination of frequency of bowel sounds after ingestion of easy-to-digest food in a complete fasting state. Experiment 2 was an investigation of how the bowel sound frequency changes according to the difference in foods eaten. Foods considered good for digestion as well as foods bad for digestion were used in Experiment 2. The results of the two experiments suggested that the frequency of bowel sounds is affected by health conditions or disruption of lifestyle rather than by differences in foods ingested.

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Enhancement of bowel sounds by wavelet-based filtering

Cited by 62 publications

References 17 publications

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds

Discrimination analysis of discontinuous breath sounds using higher-order crossings

Basic study of occurrence frequency of bowel sounds after food ingestion

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