Adaptive Threshold and Principal Component Analysis for Features Extraction of Electrocardiogram Signals

2014 ◽  
Author(s):  
Ricardo Rodriguez ◽  
Adriana Mexicano ◽  
Jiri Bila ◽  
Rafael Ponce ◽  
Salvador Cervantes
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Adaptive Threshold ◽  
Features Extraction ◽  
Electrocardiogram Signals

scholarly journals A SIGNATURE IDENTIFICATION SYSTEM WITH PRINCIPAL COMPONENT ANALYSIS AND STENTIFORD THINNING ALGORITHMS

2015 ◽  
Vol 14 (9) ◽  
pp. 6074-6084 ◽  
Author(s):  
Olatubosun Olabode ◽  
AdeniyiJide Kehinde ◽  
Akinyede Olufemi ◽  
Oluwadare A. Samuel ◽  
Fasoranbaku A. Olusoga
Keyword(s):  
Principal Component Analysis ◽  
Research Work ◽  
Principal Component ◽  
Component Analysis ◽  
Features Extraction ◽  
Design System ◽  
Manhattan Distance ◽  
Identification System ◽  
Thinning Algorithm ◽  
Handwritten Signature

Several biometric security systems have been implemented. Biometric is the use of a person’s physiological or behavioural characteristics to identify the individual. An example of behavioural method of biometric is signature identification. Signature identification is the use of handwritten signature to identify a person. This paper attempt design and implement an algorithm for handwritten signature identification. The signature identification system consists of signature acquisition, preprocessing, features extraction and matching stages. Signature acquisition can be either online or offline (both were considered in this research work). Online signatures are obtained by signing on digital tablets while offline signatures are scanned (or snapped) into the system. Preprocessing stage of the system include turning the image to greyscale. The grey image is further converted to binary (black and white). The image is then thinned, using Stentiford thinning algorithm. Stentiford thinning algorithm in an iterative thinning method with a good thinned imaged output. The image is finally cropped to rid the image of unnecessary white spaces. For features extraction, principal component analysis is used. Principal Component Analysis is a good statistical tool for identifying pattern in data. Features extracted from each signature are stored as a template. After features extraction, the distance between signature templates are computed using Manhattan distance. If the distance exceeds a certain threshold, the test signature is rejected (otherwise it is accepted). The design system has a FAR of 4% and an FRR of 6% for offline signatures. A FAR of 2% and an FRR of 3% were obtained for online signatures

scholarly journals Improvement of Fast Kurtogram Combined with PCA for Multiple Weak Fault Features Extraction

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1059
Author(s):  
Yongxing Song ◽  
Jingting Liu ◽  
Linhua Zhang ◽  
Dazhuan Wu
Keyword(s):  
Principal Component Analysis ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Component Analysis ◽  
Rotating Machinery ◽  
Features Extraction ◽  
Signal To Noise ◽  
Fault Features ◽  
Weak Fault ◽  
Noise Ratio

Demodulation plays an important role in fault feature extraction for rotating machinery. The fast kurtogram method was proved to be effective for rotating machinery demodulation. However, the demodulation effectiveness of fast kurtogram was poor for multiple fault features extraction under low signal-to-noise ratio. In this paper, an improved method of fast kurtogram, called P-kurtogram, is presented. The proposed method extracted the multiple weak fault features from multiple envelope signals-based principal component analysis. Compared with extracting features from one envelope signal of fast kurtogram, P-kurtogram showed a better demodulation performance for multiple faults. Combined with principal component analysis method, the proposed method also showed a good performance under low signal-to-noise ratio(SNR). By simulation analysis, the P-kurtogram method showed good performance for multiple modulation features extraction and robust performance in demodulation under low SNR. Then, the proposed method was demonstrated by applications of bearing faults detection and propeller detection. The results verified that the P-kurtogram has a better demodulation performance than fast kurtogram for multiple weak fault features extraction, especially under low signal-to-noise ratio. The proposed method provides a reliable basis for multiple weak fault features extraction of rotating machinery.

scholarly journals Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

2018 ◽  
Vol 2 (3) ◽  
pp. 60 ◽  
Author(s):  
Kanglin Xing ◽  
J.R.R. Mayer ◽  
Sofiane Achiche
Keyword(s):  
Principal Component Analysis ◽  
Machine Tool ◽  
Principal Component ◽  
Component Analysis ◽  
Features Extraction ◽  
State Classification ◽  
Volumetric Errors ◽  
Machine Tool Accuracy ◽  
Normal States ◽  
Five Axis

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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