The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important. Currently, the features widely used have disadvantage of dependence on the fundamental frequency extraction, being easily affected by noise and high computational complexity. In view of these shortcomings, a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed. The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands. According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence, we applied short time Fourier transform to each frequency band of the voice signal, then the feature gammatone short time spectral self-similarity (GSTS) was extracted, and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice. The experimental results showed that combined with traditional machine learning methods, GSTS reached the accuracy of 99.50% in the pathological voice database of Massachusetts Eye and Ear Infirmary (MEEI) and had an improvement of 3.46% compared with the best existing features. Also, the time of the extraction of GSTS was far less than that of traditional nonlinear features. These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.
Acoustics ; Databases, Factual ; Fourier Analysis ; Humans ; Noise ; Signal Processing, Computer-Assisted

Fourier transform infrared spectroscopic (ATR-FTIR) technique is a powerful tool for the diagnosis of several diseases. This method enables samples to be examined directly without pre-preparation. In this study, we evaluated the diagnostic value of ATR-FTIR for the detection of cancer in dogs. Cancer-bearing dogs (n = 30) diagnosed by pathologists and clinically healthy dogs (n = 40) were enrolled in this study. Peripheral blood was collected for clinicopathological diagnosis. ATR-FTIR spectra were acquired, and principal component analysis was performed on the full wave number spectra (4,000–650 cm−1). The leave-one-out cross validation technique and partial least squares regression analysis were used to predict normal and cancer spectra. Red blood cell counts, hemoglobin levels and white blood cell counts were significantly lower in cancer-bearing dogs than in clinically healthy dogs (p < 0.01, p < 0.01 and p = 0.03, respectively). ATR-FTIR spectra showed significant differences between the clinically healthy and cancer-bearing groups. This finding demonstrates that ATR-FTIR can be applied as a screening technique to distinguish between cancer-bearing dogs and healthy dogs.]]>
Animals ; Cause of Death ; Diagnosis ; Dogs ; Erythrocyte Count ; Fourier Analysis ; Incidence ; Least-Squares Analysis ; Leukocyte Count ; Mass Screening ; Methods ; Principal Component Analysis

Objective To analyze the differences among electrical damage, burns and abrasions in pig skin using Fourier transform infrared microspectroscopy （FTIR-MSP） combined with machine learning algorithm, to construct three kinds of skin injury determination models and select characteristic markers of electric injuries, in order to provide a new method for skin electric mark identification. Methods Models of electrical damage, burns and abrasions in pig skin were established. Morphological changes of different injuries were examined using traditional HE staining. The FTIR-MSP was used to detect the epidermal cell spectrum. Principal component method and partial least squares method were used to analyze the injury classification. Linear discriminant and support vector machine were used to construct the classification model, and factor loading was used to select the characteristic markers. Results Compared with the control group, the epidermal cells of the electrical damage group, burn group and abrasion group showed polarization, which was more obvious in the electrical damage group and burn group. Different types of damage was distinguished by principal component and partial least squares method. Linear discriminant and support vector machine models could effectively diagnose different damages. The absorption peaks at 2 923 cm^-1, 2 854 cm^-1, 1 623 cm^-1, and 1 535 cm^-1 showed significant differences in different injury groups. The peak intensity of electrical injury's 2 923 cm^-1 absorption peak was the highest. Conclusion FTIR-MSP combined with machine learning algorithm provides a new technique to diagnose skin electrical damage and identification electrocution.
Algorithms ; Animals ; Fourier Analysis ; Least-Squares Analysis ; Machine Learning ; Swine

BACKGROUND: Nowadays, production of nanocomposite scaffolds based on natural biopolymer, bioceramic, and metal ions is a growing field of research due to the potential for bone tissue engineering applications. METHODS: In this study, a nanocomposite scaffold for bone tissue engineering was successfully prepared using collagen (COL), beta-tricalcium phosphate (β-TCP) and strontium oxide (SrO). A composition of β-TCP (4.9 g) was prepared by doping with SrO (0.05 g). Biocompatible porous nanocomposite scaffolds were prepared by freeze-drying in different formulations [COL, COL/β-TCP (1:2 w/w), and COL/β-TCP-Sr (1:2 w/w)] to be used as a provisional matrix or scaffold for bone tissue engineering. The nanoparticles were characterized by X-ray diffraction, Fourier transforms infrared spectroscopy and energy dispersive spectroscopy. Moreover, the prepared scaffolds were characterized by physicochemical properties, such as porosity, swelling ratio, biodegradation, mechanical properties, and biomineralization. RESULTS: All the scaffolds had a microporous structure with high porosity (~ 95–99%) and appropriate pore size (100–200 µm). COL/β-TCP-Sr scaffolds had the compressive modulus (213.44 ± 0.47 kPa) higher than that of COL/β-TCP (33.14 ± 1.77 kPa). In vitro cytocompatibility, cell attachment and alkaline phosphatase (ALP) activity studies performed using rat bone marrow mesenchymal stem cells. Addition of β-TCP-Sr to collagen scaffolds increased ALP activity by 1.33–1.79 and 2.92–4.57 folds after 7 and 14 days of culture, respectively. CONCLUSION: In summary, it was found that the incorporation of Sr into the collagen-β-TCP scaffolds has a great potential for bone tissue engineering applications.
Alkaline Phosphatase ; Animals ; Biopolymers ; Bone and Bones ; Bone Marrow ; Collagen ; Fourier Analysis ; Freeze Drying ; In Vitro Techniques ; Ions ; Mesenchymal Stromal Cells ; Nanocomposites ; Nanoparticles ; Porosity ; Rats ; Spectrum Analysis ; Strontium ; X-Ray Diffraction

An ECG signal, generally filled with noise, when de-noised, enables a physician to effectively determine and predict the condition and health of the heart. This paper aims to address the issue of denoising a noisy ECG signal using the Fast Fourier Transform based bandpass filter. Multi-stage adaptive peak detection is then applied to identify the R-peak in the QRS complex of the ECG signal. The result of test simulations using the MIT/BIH Arrhythmia database shows high sensitivity and positive predictivity (PP) of 99.98 and 99.96% respectively, confirming the accuracy and reliability of proposed algorithm for detecting R-peaks in the ECG signal.
Arrhythmias, Cardiac ; Electrocardiography ; Fourier Analysis ; Heart ; Noise

This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstructured interstitial) were obtained from 512 thoracic radiographs of 252 dogs and 65 cats. Forty-four texture parameters based on eight methods of texture analysis (first-order statistics, spatial gray-level-dependence matrices, gray-level-difference statistics, gray-level run length image statistics, neighborhood gray-tone difference matrices, fractal dimension texture analysis, Fourier power spectrum, and Law's texture energy measures) were used to extract textural features from the ROIs. The texture parameters of each lung pattern were compared and used for training and testing of artificial neural networks. Classification performance was evaluated by calculating accuracy and the area under the receiver operating characteristic curve (AUC). Forty texture parameters showed significant differences between the lung patterns. The accuracy of lung pattern classification was 99.1% in the training dataset and 91.9% in the testing dataset. The AUCs were above 0.98 in the training set and above 0.92 in the testing dataset. Texture analysis and machine learning algorithms may potentially facilitate the evaluation of medical images.
Animals ; Area Under Curve ; Cats ; Classification ; Dataset ; Dogs ; Fourier Analysis ; Fractals ; Lung ; Machine Learning ; Neural Networks (Computer) ; Pattern Recognition, Visual ; Radiography, Thoracic ; Residence Characteristics ; ROC Curve

BACKGROUND: Alendronate (AL), a drug for inhibiting osteoclast-mediated bone-resorption, was intercalated into an inorganic drug delivery nanovehicle, layered double hydroxide (LDH), to form a new nanohybrid, AL-LDH, with 1:1 heterostructure along the crystallographic C-axis. Based on the intercalation reaction strategy, the present AL-LDH drug delivery system (DDS) was realized with an enhanced drug efficacy of AL, which was confirmed by the improved proliferation and osteogenic differentiation of osteoblast-like cells (MG63). METHODS: The AL-LDH nanohybrid was synthesized by conventional ion-exchange reaction and characterized by powder X-ray diffraction (PXRD), high-resolution transmission electron microscopy (HR-TEM), and Fourier transform infrared (FT-IR) spectroscopy. Additionally, in vitro efficacy tests, such as cell proliferation and alkaline phosphatase (ALP) activity, were analyzed. RESULTS: The AL was successfully intercalated into LDH via ion-exchange reaction, and thus prepared AL-LDH DDS was X-ray single phasic and chemically well defined. The accumulated AL content in MG63 cells treated with the AL-LDH DDS nanoparticles was determined to be 10.6-fold higher than that within those treated with the intact AL after incubation for 1 hour, suggesting that intercellular permeation of AL was facilitated thanks to the hybridization with drug delivery vehicle, LDH. Furthermore, both in vitro proliferation level and ALP activity of MG63 treated with the present hybrid drug, AL-LDH, were found to be much more enhanced than those treated with the intact AL. This is surely due to the fact that LDH could deliver AL drug very efficiently, although LDH itself does not show any effect on proliferation and osteogenic differentiation of MG63 cells. CONCLUSION: The present AL-LDH could be considered as a promising DDS for improving efficacy of AL.
Alendronate ; Alkaline Phosphatase ; Cell Proliferation ; Drug Delivery Systems ; Fourier Analysis ; In Vitro Techniques ; Microscopy, Electron, Transmission ; Nanoparticles ; Spectrum Analysis ; X-Ray Diffraction

The ultrasound Doppler fetal heart rate measurement is the gold standard of fetal heart rate counting. However, the existing fetal heart rate extraction algorithms are not designed specifically to suppress the high maternal interference during the second stage of labor, and false detection occurrences are common during labor. With this background, a method combining time-frequency frame template library optimal selecting and non-linear template matching is proposed. The method contributes a template library, and the optimal template can be selected to match the signal frame. After the short-time Fourier transform of the signal, the difference between the signal and the template is optimized by leaky rectified linear unit (LReLU) function frame by frame. The heart rate was calculated from the peak of the matching curve and the heart rate was calculated. By comparing the proposed method with the autocorrelation method, the results show that the detection accuracy of the proposed method is improved by 20% on average, and the non-linear template matching of 23% samples is at least 50% higher than the autocorrelation method. This paper designs the algorithm by analyzing the characteristics of the interference and signal mixing. We hope that this paper will provide a new idea for fetal heart rate extraction which not only focuses on the original signal.
Algorithms ; Female ; Fetal Monitoring ; Fourier Analysis ; Heart Rate, Fetal ; Humans ; Pregnancy ; Signal Processing, Computer-Assisted ; Ultrasonography, Doppler

BACKGROUND: Using analysis of air samples from the workplace, we report on one case of pneumoconiosis in an individual who has been working in a polytetrafluoroethylene (PTFE) spraying process for 28 years. CASE PRESENTATION: The patient was diagnosed with granulomatous lung disease caused by PTFE using computed tomography (CT), lung biopsy and electron microscopy. To assess the qualitative and quantitative exposure to PTFE in workplace, Fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX) and thermogravimetric analysis (TGA) were performed on air samples from the workplace. The presence of PTFE particles was confirmed, and the airborne concentration of PTFE was estimated to be 0.75 mg/m3. CONCLUSIONS: This case demonstrates that long-term exposure to PTFE spraying can cause granulomatous lung lesions such as pneumoconiosis; such lesions appear to be caused not by the degradation products of PTFE from high temperatures but by spraying the particles of PTFE. Along with air-sampling analysis, we suggest monitoring the concentration of airborne PTFE particles related to chronic lung disease.
Biopsy ; Humans ; Hygiene ; Lung ; Lung Diseases ; Microscopy, Electron ; Occupational Diseases ; Pneumoconiosis ; Polytetrafluoroethylene ; Spectroscopy, Fourier Transform Infrared ; Spectrum Analysis

The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.
Classification ; Dataset ; Diagnosis ; Fourier Analysis ; Heart Diseases ; Heart Sounds ; Heart ; Sensitivity and Specificity