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Shouzhong Xiao

Publications and source records attributed to Shouzhong Xiao.

9 recordsLinked to original sources
PubMed2006

[Heart sound recognition algorithm based on PNN for evaluating cardiac contractility change trend].

This paper discusses the recognition of heart sound for evaluating the cardiac contractility change trend, which includes heart sound samples recorded at different exercise condition. Especially, focused on the recognition of heart sound recorded after high intensity exercise workload. The algorithm proposed consisted of two correlative methods. The first was to recognize heart sound recorded at rest and after low intensity exercise workloads by probabilistic neural network and the second was to recognize heart sound recorded after high intensity exercise workloads based on the characteristic of heart sound. Both methods have two consecutive phases. Firstly, all peaks, including the peaks of both heart sounds and noise, are marked by a repetitive threshold detecting algorithm. Secondly, probabilistic neural network is employed to classify the peaks detected in the first phase into Si, S2, and noise. Finally, the performance of the algorithm was evaluated using 45 digital heart sound recordings including normal and abnormal heart sound, which were recorded at rest and after low intensity exercise workloads, and 28 digital heart sound recordings recorded after high intensity exercise workloads. The results showed that over 94% of heart sound samples were classified and recognized correctly. Moreover, the reasons for the wrong classification, of which omitting and misdetection are two main problems, are also discussed and solutions are proposed. So this method can be improved and refined in following studies. In conclusion, this algorithm is a reliable approach to detect and classify heart sounds, providing a solid basis for further heart sound analysis.

Algorithms↗
PubMed2005

[Exercise ECG signal de-noising using unbiased risk estimate and wavelet transform].

In this paper a filtering method for EECG (Exercise ECG) signal is proposed which is based on wavelet transform (WT) and Stein's unbiased risk estimate (SURE). This algorithm was used to decompose original EECG signals into detail signals on different frequency bands by using WT and get different thresholds with SURE. According to EECG signal features and by using the above thresholds, the method amended several detail signals so that the main interferences in EECG signal can be removed efficiently. The authors also put forward two indexes to estimate the validity of such algorithms. Our experimental results demonstrate that this is an efficient de-noising method for EECG.

Algorithms↗
PubMed2004

The internet-based knowledge acquisition and management method to construct large-scale distributed medical expert systems.

The Internet offers an unprecedented opportunity to construct powerful large-scale medical expert systems (MES). In these systems, a cost-effective medical knowledge acquisition (KA) and management scheme is highly desirable to handle the large quantities of, often conflicting, medical information collected from medical experts in different medical fields and from different geographical regions. In this paper, we demonstrate that a medical KA/management system can be built upon a three-tier distributed client/server architecture. The knowledge in the system is stored/managed in three knowledge bases. The maturity of the medical know-how controls the knowledge flow through these knowledge bases. In addition, to facilitate the knowledge representation and application in these knowledge bases as well as information retrieval across the Internet, an 8-digit numeric coding scheme with a weight value system is proposed. At present, a medical KA and management system based on the proposed method is being tested in clinics. Current results have showed that the method is a viable solution to construct, modify, and expand a distributed MES through the Internet.

Computer Systems↗
PubMed2004

[Study on medical diagnosis decision support system for heart diseases based on hybrid genetic algorithm].

In this study, a medical diagnosis decision support system based on hybrid genetic algorithm has been established to support the diagnosis of five common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertensive heart disease, chronic cor pulmonale and congenital heart disease). A heart disease database consisting of 352 samples was used for constructing and testing the performance of system. Cross-validation of the experimental results indicate that the system we established shows high capability of classifying these five kinds of heart diseases, the mean accuracy of classification is as high as 90.6%, and the user accuracy and procedure accuracy of each disease are both above 85.0%, showing great application prospect of supporting heart diseases diagnosis in clinics.

Algorithms↗
PubMed2004

[Heart sound recognition algorithm based on mathematical morphology].

In this paper, a new method was put forward for automatic recognition of the first heart sounds (S1) and the second heart sounds (S2). After the original heart sound signal was preprocessed, the heart sound envelope was extracted by using the mathematical morphology. Then on the heart sound envelope, S1 and S2 were recognized. Eighty heart sound samples collected were used for testing the algorithm. The accuracy of recognition was 86%, and was 100% for the normal heart sound. The result showed that the algorithm proposed in this paper had high performance, which could be used as a basis for further analysis of heart sound.

Algorithms↗
PubMed2002

A relative value method for measuring and evaluating cardiac reserve.

BACKGROUND: Although a very close relationship between the amplitude of the first heart sound (S1) and the cardiac contractility have been proven by previous studies, the absolute value of S1 can not be applied for evaluating cardiac contractility. However, we were able to devise some indicators with relative values for evaluating cardiac function. METHODS: Tests were carried out on a varied group of volunteers. Four indicators were devised: (1) the increase of the amplitude of the first heart sound after accomplishing different exercise workloads, with respect to the amplitude of the first heart sound (S1)recorded at rest was defined as cardiac contractility change trend (CCCT). When the subjects completed the entire designed exercise workload (7000 J), the resulting CCCT was defined as CCCT(1); when only 1/4 of the designed exercise workload was completed, the result was defined as CCCT(1/4). (2) The ratio of S1 amplitude to S2 amplitude (S1/S2). (3) The ratio of S1 amplitude at tricuspid valve auscultation area to that at mitral auscultation area T1/M1 (4) the ratio of diastolic to systolic duration (D/S). Data were expressed as mean +/- SD. RESULTS: CCCT(1/4) was 6.36 +/- 3.01 (n = 67), CCCT(1) was 10.36 +/- 4.2 (n = 33), S1/S2 was 1.89 +/- 0.94 (n = 140), T1/M1 was 1.44 +/- 0.99 (n = 144), and D/S was 1.68 +/- 0.27 (n = 172). CONCLUSIONS: Using indicators CCCT(1/4) and CCCT(1) may be beneficial for evaluating cardiac contractility and cardiac reserve mobilization level, S1/S2 for considering the factor for hypotension, T1/M1 for evaluating the right heart load, and D/S for evaluating diastolic cardiac blood perfusion time.

Adolescent↗
PubMed2002

[Experimental study on an auditory method for analyzing DNA segments].

To explore a new method for analyzing biological molecules that have already been sequenced, an experimental study on an auditory method was carried out. The auditory method for analyzing biological molecules includes audible representation of sequence data. Audible representation of sequence data was implemented by using a multimedia computer. Each mononucleotide in a DNA sequence was matched with a corresponding sound, i.e., a DNA sequence was "dubbed" in a sound sequence. When the sound sequence is played, a special cadence can be heard. In the audible representation experiment, special cadences of different exons can be clearly heard. The results show that audible representation of DNA sequence data can be implemented by using a multimedia technique. After a 5-time auditory training, subjects both in internal testing and external testing can obtain 93%-100% of judgment accuracy rate for the difference between two sound sequences of two different exons, thus providing an experimental basis for the practicability of this method. Auditory method for analyzing DNA segments might be beneficial for the research in comparative genomics and functional genomics. This new technology must be robust and be carefully evaluated and improved in a high-throughput environment before its implementation in an application setting.

Computational Biology↗