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PubMed1999

A pacemaker working status telemonitoring algorithm.

To extend the application of a previously developed home ECG and blood pressure telemonitoring system for pacemaker users, a specially designed pacemaker working status analysis algorithm is developed in this paper. This paper includes four sections. First, a pacing beat and spontaneous beat recognition algorithm are established in which a FIR filter and three threshold methods are used to pick up the pacing spikes and a dynamic threshold modification method is proposed for error elimination. Second, a beat classification algorithm is developed based on decision rules for six commonly used pacemaker types. Third, a pacemaker malfunction recognition algorithm is established to evaluate the pacemaker working status. Fourth, the statistics of the analysis and the report are also provided. To validate the algorithm, clinical data were collected and manually classified by specialists. Then, the classification and recognition results were obtained with the clinical data. Results show that the algorithm can achieve a recognition rate of 98.4%. Results also indicate that the algorithm is capable of working in real-time speed. The algorithm has been incorporated into the telemonitoring system, and trial application cases are also reported.

Algorithms↗
PubMed2004

An approach to identifying osteopenic women at increased short-term risk of fracture.

BACKGROUND: Identification and management of women to reduce fractures is often limited to T scores less than -2.5, although many fractures occur with higher T scores. We developed a classification algorithm that identifies women with osteopenia (T scores of -2.5 to -1.0) who are at increased risk of fracture within 12 months of peripheral bone density testing. METHODS: A total of 57 421 postmenopausal white women with baseline peripheral T scores of -2.5 to -1.0 and 1-year information on new fractures were included. Thirty-two risk factors for fracture were entered into a classification and regression tree analysis to build an algorithm that best predicted future fracture events. RESULTS: A total of 1130 women had new fractures in 1 year. Previous fracture, T score at a peripheral site of -1.8 or less, self-rated poor health status, and poor mobility were identified as the most important determinants of short-term fracture. Fifty-five percent of the women were identified as being at increased fracture risk. Women with previous fracture, regardless of T score, had a risk of 4.1%, followed by 2.2% in women with T scores of -1.8 or less or with poor health status, and 1.9% for women with poor mobility. The algorithm correctly classified 74% of the women who experienced a fracture. CONCLUSIONS: This classification tool accurately identified postmenopausal women with peripheral T scores of -2.5 to -1.0 who are at increased risk of fracture within 12 months. It can be used in clinical practice to guide assessment and treatment decisions.

Age Distribution↗
PubMed2002

A new approach to finding natural chemical structure classes.

In modern drug discovery, large compound libraries need to be compared and the diversity of compound libraries needs to be analyzed. Classification algorithms are important tools for accomplishing these tasks. In this paper, a chemical structural scaffold based classification approach is reported. The goals of the approach are to find natural structure families from a large (millions of entries) compound library within a feasible time period and to view the library in two-dimensional data space using chemically meaningful methods.

Algorithms↗
PubMed2003

A novel approach toward development of a rapid blood test for breast cancer.

Mammography remains the diagnostic test of choice for breast cancer, but 20% of cancers still go undetected. Many serum biomarkers have been reported for breast cancer but none have proven to represent effective diagnostic strategies. ProteinChip mass spectrometry is an innovative technology that searches the proteome for differentially expressed proteins, allowing for the creation of a panel or profile of biomarkers. The objective of this study was to construct unique cancer-associated serum profiles that, combined with a classification algorithm, would enhance the detection of breast cancer Pretreatment serum samples from 134 female patients (45 with cancer, 42 with benign disease, 47 normal) were procured prospectively following institutional review board-approved protocols. Proteins were denatured, applied onto ProteinChip affinity surfaces, and subjected to surface enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. The SELDI output was analyzed using Biomarker Pattern Software to develop a classification tree based on group-specific protein profiles. The cross-validation analysis of cancer versus normal revealed sensitivity and specificity rates of 80% and 79%, and for cancer versus benign disease, 78% and 83%, respectively. When 2 different chip surfaces were combined the sensitivity and specificity increased to 90% and 93%, respectively. The sensitivity and specificity of this technique are comparable to those of mammography and, if confirmed in a larger study, this technique could provide the means toward development of a simple blood test to aid in the early detection of breast cancer. The combination of SELDI ProteinChip mass spectrometry and a classification- and regression-tree algorithm has the potential to use serum protein expression profiles for detection and diagnosis of breast cancer.

Adult↗
PubMed2006

Tumor classification based on DNA copy number aberrations determined using SNP arrays.

High-density single nucleotide polymorphism (SNP) array is a recently introduced technology that genotypes more than 10,000 human SNPs on a single array. It has been shown that SNP arrays can be used to determine not only SNP genotype calls, but also DNA copy number (DCN) aberrations, which are common in solid tumors. In the past, effective cancer classification has been demonstrated using microarray gene expression data, or DCN data derived from comparative genomic hybridization (CGH) arrays. However, the feasibility of cancer classification based on DCN aberrations determined by SNP arrays has not been previously investigated. In this study, we address this issue by applying state-of-the-art classification algorithms and feature selection algorithms to the DCN aberration data derived from a public SNP array dataset. Performance was measured via leave-one-out cross-validation (LOOCV) classification accuracy. Experimental results showed that the maximum accuracy was 73.33%, which is comparable to the maximum accuracy of 76.5% based on CGH-derived DCN data reported previously in the literature. These results suggest that DCN aberration data derived from SNP arrays is useful for etiology-based tumor classification.

Algorithms↗
PubMed1995

A rough sets approach to patient classification in medical records.

This study used the original model of rough sets for data analysis of objective clinical findings from pneumonia patients. Rough sets data analysis was used to draw data dependencies, data reductions, approximate set classifications, and rule inductions from records in a clinical database. This study utilized Pawlak's rough classification algorithm [1] to examine the relationship between a constellation of findings and the ICD-9-CM classification scheme used for disease coding in order to generate a reduct. The reduct is a logical construct of the most information-preserving findings from a decision table. The Iliad expert system, which is based on Bayes Theorem, was used to validate the results we obtained using rough sets analysis to discriminate between the different types of pneumonia in the patient population. From a set of 25 objective clinical findings, chosen because they had a positive or negative predictive value for probability of death for pneumonia patients, the rough sets analysis constructed a logical classifier of six attributes that was as discriminatory as the statistical classifier in Iliad which utilized 11 of those 25 attributes. The rough sets methods are capable of identifying a minimal set of attribute values that are associated with a disease label. These methods could be applied to develop more consistent labeling methods, and, potentially flag activities which do not contribute to diagnostic labeling or health results.

Bayes Theorem↗
PubMed2004

[An algorithm study on telecardiogram diagnosis based on multivariate autoregressive model and two-lead ECG signals].

OBJECTIVE: In view of the time delay caused by reconstruction of signals at remote sites, a direct classification method with high accuracy suitable for telediagnosis of electrocardiogram (ECG) signals is studied. METHOD: The data for analysis and classification was obtained from MIT-BIH database, including 300 samples each of normal sinus rhythm (NSR), atria premature contraction (APC), premature ventricular contraction (PVC), ventricular tachycardia (VT), ventricular fibrillation (VF) and superventricular tachycardia (SVT). An multivariate autoregressive (MAR) model based technique that could combine the signals of two ECG leads was presented to classify the ECGs directly, including MAR modeling performed on ECGs, and quadratic discrimination function (QDF) based classification by using MAR coefficients and K-L MAR coefficients. RESULT: Besides quick and convenient diagnosis, the accuracy of the proposed classification algorithm was as high as 98.3%-100%. CONCLUSION: The MAR modeling based technique is suitable for telecardiogram diagnosis. Comparing with single-lead ECGs, better classification results can be obtained through the combination of two-lead ECG signals.

Algorithms↗
PubMed2025

N6-methyladenine identification using deep learning and discriminative feature integration.

N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection and understand its biological significance. Despite the rapid progress of machine learning in bioinformatics, accurately detecting 6 mA sites remains a challenge due to the limited generalizability and efficiency of existing approaches. In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). To optimize computational efficiency and eliminate irrelevant or noisy features, an unsupervised Principal Component Analysis (PCA) algorithm is employed, ensuring the selection of the most informative features. A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. The robustness and generalizability of Deep-N6mA were rigorously validated using fivefold cross-validation on two benchmark datasets. Experimental results reveal that Deep-N6mA achieves an average accuracy of 97.70% on the F. vesca dataset and 95.75% on the R. chinensis dataset, outperforming existing methods by 4.12% and 4.55%, respectively. These findings underscore the effectiveness of Deep-N6mA as a reliable tool for early 6 mA site detection, contributing to epigenetic research and advancing the field of computational biology.

Deep Learning↗
PubMed2005

[The application of surface-enhanced laser desorption/ionization-time of flight-mass spectrometry in diagnosing dysplasia and chronic atrophic gastric-carditis in population with high risk of gastric-cardia adenocarcinoma].

OBJECTIVES: To evaluate the serum biomarkers for diagnosis of gastric cardia dysplasia (DYS) and chronic atrophic gastric-carditis (CAG) and to provide a novel screening method for high risk population of gastric-cardia adenocarcinoma (GCA). METHODS: Proteomic spectra were generated by surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) and weak cation exchange protein chip system. A set of spectra derived from analysis of serum from 143 symptom-free subjects at high-risk area for GCA, including 63 cases with histologically normal gastric cardia epithelia, 57 of CAG and 23 of DYS, were analyzed by bioinformatics like decision tree classification algorithm. The sensitivity and the specificity for test group were performed by using 10-fold cross validation classification with the decision tree classification model. RESULTS: One protein spot with a ratio of mass to charge (M/Z) of M3894. 0 was selected to build a decision tree classification model to identify the case with DYS or normal. With this classification model, the sensitive rate for DYS identification was 87% (20/23). Two proteins with M/Z of M2942. 15 and M33316. 6 were used to build a decision tree classification model. With this model, the sensitivity for discriminating CAG from normal was 93% (53/57) and the specificity was 92 (58/63). CONCLUSIONS: The gastric cardia lesions of DYS and CAG could be identified by SELDI-TOF-MS technique specifically in symptom-free subjects at high incidence area for GCA. The present findings provide a new screening way for high-risk subjects with CGA.

Adult↗
PubMed2006

Spectral embedding finds meaningful (relevant) structure in image and microarray data.

BACKGROUND: Accurate methods for extraction of meaningful patterns in high dimensional data have become increasingly important with the recent generation of data types containing measurements across thousands of variables. Principal components analysis (PCA) is a linear dimensionality reduction (DR) method that is unsupervised in that it relies only on the data; projections are calculated in Euclidean or a similar linear space and do not use tuning parameters for optimizing the fit to the data. However, relationships within sets of nonlinear data types, such as biological networks or images, are frequently mis-rendered into a low dimensional space by linear methods. Nonlinear methods, in contrast, attempt to model important aspects of the underlying data structure, often requiring parameter(s) fitting to the data type of interest. In many cases, the optimal parameter values vary when different classification algorithms are applied on the same rendered subspace, making the results of such methods highly dependent upon the type of classifier implemented. RESULTS: We present the results of applying the spectral method of Lafon, a nonlinear DR method based on the weighted graph Laplacian, that minimizes the requirements for such parameter optimization for two biological data types. We demonstrate that it is successful in determining implicit ordering of brain slice image data and in classifying separate species in microarray data, as compared to two conventional linear methods and three nonlinear methods (one of which is an alternative spectral method). This spectral implementation is shown to provide more meaningful information, by preserving important relationships, than the methods of DR presented for comparison. Tuning parameter fitting is simple and is a general, rather than data type or experiment specific approach, for the two datasets analyzed here. Tuning parameter optimization is minimized in the DR step to each subsequent classification method, enabling the possibility of valid cross-experiment comparisons. CONCLUSION: Results from the spectral method presented here exhibit the desirable properties of preserving meaningful nonlinear relationships in lower dimensional space and requiring minimal parameter fitting, providing a useful algorithm for purposes of visualization and classification across diverse datasets, a common challenge in systems biology.

Algorithms↗
PubMed2003

Robust classifier for the automated detection of ammonia in heated plumes by passive fourier transform infrared spectrometry.

An automated classification algorithm is implemented for the detection of ammonia vapor in heated plumes by passive Fourier transform infrared (FT-IR) spectrometry. This classification methodology allows the real-time detection of chemical signatures in gaseous effluents such as those generated from industrial processes. The characteristics of real-time implementation and excellent robustness are achieved by an analysis strategy based on the application of band-pass digital filters to short segments of the interferogram data collected by the FT-IR spectrometer, followed by the use of piecewise linear discriminant analysis to obtain a yes/no classification regarding the presence of the analyte signature in the filtered data. The optimal classifier developed through this work is based on only 110 interferogram points and employs a single band-pass filter centered at 945 cm(-)(1) with a pass-band full width at half-maximum of 93 cm(-)(1). The average stop-band attenuation of the optimal filter is 42.1 dB. The robustness of the algorithm is tested by exposing it to chemical releases of sulfur hexafluoride, ethanol, methanol, sulfur dioxide, and hydrogen chloride that were not included in the development of the classifier. Excellent classification performance is demonstrated, with missed ammonia detections occurring at a rate of approximately 1%. The occurrence of false detections is less than 0.1% for SF(6) and less than 0.02% for the other interferences tested.

Air Pollutants, Occupational↗
PubMed1992

Automatic computer analysis of transients in EEG.

The electroencephalogram (EEG) is often used for the diagnosis of diseases and functional disturbances in the brain. In this paper, new algorithms developed for the automatic detection of transients in EEG are described. The single spike, and spike and wave bursts, both of which are abnormal phenomena associated with epileptic activity are considered. The algorithms for detecting these transients were tested using real EEG data. The transient detection is enhanced by two classification algorithms: patient-independent analysis and patient-dependent analysis. In the patient-independent analysis, multiple reference templates are generated from a patient population and for the patient-dependent analysis, the spikes from the patient's own EEG recording is used as reference. The description of the algorithms and their performances are presented.

Algorithms↗
PubMed1993

Neural network approach for computer-assisted interpretation of ultrasound images of the gallbladder.

Multi-formatted films of 90 ultrasound examinations of the gallbladder (stones 56 cases, sludge 20 cases, hydrops five cases, normal gallbladder nine cases) have been digitalized and stored in a personal computer. Image data of each examination was processed to extract a 19-dimensional vector that represents the essential diagnostic information of each examination. This vector was evaluated by three different classification algorithms: (1) classical nearest neighbor principle, (2) classical linear discriminant analysis, (3) multilayered backpropagation neural network. The correct classification rate was 64% (58/90) for the nearest neighbor principle, 97% (87/90) for the linear discriminant analysis, and 99% (89/90) for the backpropagation neural network. We conclude that, (1) automated classification of ultrasound images is possible for limited diagnostic problems, (2) a neural network approach can be used successfully for that goal, and (3) the efficiency of the more flexible neural network approach is comparable to large-scale classical methods.

Cholecystography↗
PubMed2005

Validation of automatically classified magnetic resonance images for carotid plaque compositional analysis.

BACKGROUND AND PURPOSE: MRI may be used for noninvasive assessment of atherosclerotic lesions; however, MRI evaluation of plaque composition requires validation against an accepted reference standard, such as the American Heart Association (AHA) lesion grade, defined by histopathological examination. METHODS: Forty-eight carotid endarterectomy specimen cross-sections had AHA lesion grade determined histopathologically and were concurrently imaged using combinations of 8 MRI contrast weightings in vitro. A maximum likelihood classification algorithm generated MRI "maps" of plaque components, and an AHA lesion grade was assigned correspondingly. Additional analyses compared classification accuracy obtained with a commonly used set of magnetic resonance contrast weightings [proton density (PDw), T1 (T1w), and partial T2 (T2w)] to accuracy obtained with the combination of PDw, T1w, and diffusion-weighted (Dw) contrast. RESULTS: For the 8-contrast combination, the sensitivities for fibrous tissue, necrotic core, calcification, and hemorrhage detection were 83%, 67%, 86%, and 77%, respectively. The corresponding specificities were 81%, 78%, 99%, and 97%. Good agreement (79%) between magnetic resonance and histopathology for AHA classification was achieved. For the PDw, T1w, and Dw combination, the overall classification accuracy was insignificantly different at 78%, whereas the overall classification accuracy using PDw, T1w, and partial T2w contrast weightings was significantly lower at 67%. CONCLUSIONS: This study provides proof-of-principle that the composition of atherosclerotic plaques determined by automated classification of high-resolution ex vivo MRI accurately reflects lesion composition defined by histopathological examination.

Atherosclerosis↗
PubMed1998

Robust supervised classification of motor unit action potentials.

A certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit potential trian (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape nonstationarity.

Algorithms↗
PubMed2005

Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method.

MOTIVATION: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. RESULTS: We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins. AVAILABILITY: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ CONTACT: jul@ssu.ac.kr.

Algorithms↗
PubMed2005

An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue.

The automatic segmentation of nuclei in confocal reflectance images of cervical tissue is an important goal toward developing less expensive cervical precancer detection methods. Since in vivo confocal reflectance microscopy is an emerging technology for cancer detection, no prior work has been reported on the automatic segmentation of in vivo confocal reflectance images. However, prior work has shown that nuclear size and nuclear-to-cytoplasmic ratio can determine the presence or extent of cervical precancer. Thus, segmenting nuclei in confocal images will aid in cervical precancer detection. Successful segmentation of images of any type can be significantly enhanced by the introduction of accurate image models. To enable a deeper understanding of confocal reflectance microscopy images of cervical tissue, and to supply a basis for parameter selection in a classification algorithm, we have developed a model that accounts for the properties of the imaging system and of the tissues. Using our model in conjunction with a powerful image enhancement tool (anisotropic median-diffusion), appropriate statistical image modeling of spatial interactions (Gaussian Markov random fields), and a Bayesian framework for classification-segmentation, we have developed an effective algorithm for automatically segmenting nuclei in confocal images of cervical tissue. We have applied our algorithm to an extensive set of cervical images and have found that it detects 90% of hand-segmented nuclei with an average of 6 false positives per frame.

Algorithms↗
PubMedDate not supplied

QSAR in ecotoxicity: an overview of modern classification techniques.

This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.

Algorithms↗