A robust heart sounds segmentation module based on S-transform

This paper presents a new module for heart sounds segmentation based on S-transform. The heart sounds segmentation process segments the PhonoCardioGram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2. An original localization method of heart sounds are proposed in this study. The method named SSE calculates the Shannon energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2. The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. To distinguish between S1 and S2, a feature extraction method based on the singular value decomposition (SVD) of the S-matrix is applied in this study. The proposed segmentation module is evaluated at each block according to a database of 80 sounds, including 40 sounds with cardiac pathologies.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

A new model which is capable of generating realistic synthetic phonocardiogram (PCG) signals is introduced based on three coupled ordinary differential equations. The new PCG model takes into account the respiratory frequency, the heart rate variability and the time splitting of first and second heart sounds. This time splitting occurs with each cardiac cycle and varies with inhalation and exhalation. Clinical PCG statistics and the close temporal relationship between events in ECG and PCG are used to deduce values of PCG model parameters.In comparison with published PCG models, the proposed model allows a larger number of known PCG features to be taken into consideration. Moreover it is able to generate both normal and abnormal realistic synthetic heart sounds. Results show that these synthetic PCG signals have the closest features to those of a conventional heart sound in both time and frequency domains. Additionally, a sound quality test carried out by eight cardiologists demonstrates that the proposed model outperforms the existing models.This new PCG model is promising and useful in assessing signal processing techniques which are developed to help clinical diagnosis based on PCG.     

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.     

基于MFCC和GMM的昆虫声音自动识别

昆虫的运动、 取食、 鸣叫都会发出声音, 这些声音存在种内相似性和种间差异性, 因此可用来识别昆虫的种类。基于昆虫声音的昆虫种类自动检测技术对协助农业和林业从业人员方便地识别昆虫种类非常有意义。本研究采用了语音识别领域里的声音参数化技术来实现昆虫的声音自动鉴别。声音样本经预处理后, 提取梅尔倒谱系数（Mel frequency cepstrum coefficient, MFCC）作为特征, 并用这些样本提取的MFCC特征集训练混合高斯模型（Gaussian mixture model, GMM）。最后用训练所得到的GMM对未知类别的昆虫声音样本进行分类。该方法在包含58种昆虫声音的样本库中进行了评估, 取得了较高的识别正确率（平均精度为98.95%）和较理想的时间性能。该测试结果证明了基于MFCC和GMM的语音参数化技术可以用来有效地识别昆虫种类。     

Are minidisc recorders adequate for the study of respiratory sounds?

Digital audio tape (DAT) recorders have become the de facto gold standard recording devices for lung sounds. Sound recorded on DAT is compact-disk (CD) quality with adequate sensitivity from below 20 Hz to above 20 KHz. However, DAT recorders have drawbacks. Although small, they are relatively heavy, the recording mechanism is complex and delicate, and finding one desired track out of many is inconvenient. A more recent development in portable recording devices is the minidisc (MD) recorder. These recorders are widely available, inexpensive, small and light, rugged, mechanically simple, and record digital data in tracks that may be named and accessed directly. Minidiscs hold as much recorded sound as a compact disk but in about 1/5 of the recordable area. The data compression is achieved by use of a technique known as adaptive transform acoustic coding for minidisc (ATRAC). This coding technique makes decisions about what components of the sound would not be heard by a human listener and discards the digital information that represents these sounds. Most of this compression takes place on sounds above 5.5 KHz. As the intended use of these recorders is the storage and reproduction of music, it is unknown whether ATRAC will discard or distort significant portions of typical lung sound signals. We determined the suitability of MD recorders for respiratory sound research by comparing a variety of normal and pathologic lung sounds that were digitized directly into a computer and also after recording by a DAT recorder and 2 different MD recorders (Sharp and Sony). We found that the frequency spectra and waveforms of respiratory sounds were not distorted in any important way by recording on the two MD recorders tested.     

Neurogenesis or neuronal specification: phosphorylation strikes again!

One way to localize sounds is to measure differences in sound intensity at the two ears. This comparison is made in the lateral superior olive, where signals from both ears converge. Magnusson et al. in this issue of Neuron show that dendritic GABA release can regulate this comparison, which may allow animals localizing sounds to adapt to listening conditions.     

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS–wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).     

A method for computerized modification of certain natural animal sounds for communication study purposes

A new method for computerized modification of sound signals is presented. With digital signal processing in the time domain it is possible to alter the amplitude, the frequency and the time scale of natural sounds independently. The method can be applied to natural sounds with reasonably pure tonal quality.     

Noninvasive diagnosis of coronary artery disease using a neural network algorithm

This study examines the utility of neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions. It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on the poles and power spectral density function (PSD) of the Autoregressive (AR) method after Adaptive Line Enhancement (ALE) method. In addition, some physical examination variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network. The analysis was studied on one hundred recordings (63 abnormal, 37 normals). The network correctly identified 84% of the subjects with coronary artery disease and 89% of the normal subjects.     

The selection of introduced sounds to improve the soundscape in the public spaces

The purpose of introducing sounds is to afford a comfortable acoustic environment and to design good soundscapes. This study aims at rating the preference of subjects for the introduced sounds suitable to the public spaces and also investigates the methodology to select the sounds by subjective and objective procedures. Seventeen kinds of the introduced sounds were evaluated with nine adjectives in the presence of visual location information. Also, adequate sound levels were calculated by adjusting the volume of introduced sounds in the presence of the actual background sounds of locations and visual information. The concept of harmony with the surroundings was reviewed by analyzing the correlation among 9 adjectives which express introduced sounds. And the effectiveness of existed sound quality index was analyzed so as to select the introduced sounds quantitatively. By the evaluation of the adequate level of the introduced sounds, it is proposed that the lower introduced sound level would be better for the noisy circumstances.     

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.     

Flowers respond to pollinator sound within minutes by increasing nectar sugar concentration

Can plants sense natural airborne sounds and respond to them rapidly? We show that Oenothera drummondii flowers, exposed to playback sound of a flying bee or to synthetic sound signals at similar frequencies, produce sweeter nectar within 3 min, potentially increasing the chances of cross pollination. We found that the flowers vibrated mechanically in response to these sounds, suggesting a plausible mechanism where the flower serves as an auditory sensory organ. Both the vibration and the nectar response were frequency‐specific: the flowers responded and vibrated to pollinator sounds, but not to higher frequency sound. Our results document for the first time that plants can rapidly respond to pollinator sounds in an ecologically relevant way. Potential implications include plant resource allocation, the evolution of flower shape and the evolution of pollinators sound. Finally, our results suggest that plants may be affected by other sounds as well, including anthropogenic ones.     

An algorithm for FHR estimation from foetal phonocardiographic signals

The long-term foetal surveillance is often to be recommended. Hence, the fully non-invasive acoustic recording, through maternal abdomen, represents a valuable alternative to the ultrasonic cardiotocography. Unfortunately, the recorded heart sound signal is heavily loaded by noise, thus the determination of the foetal heart rate raises serious signal processing issues. In this paper, we present a new algorithm for foetal heart rate estimation from foetal phonocardiographic recordings. A filtering is employed as a first step of the algorithm to reduce the background noise. A block for first heart sounds enhancing is then used to further reduce other components of foetal heart sound signals. A complex logic block, guided by a number of rules concerning foetal heart beat regularity, is proposed as a successive block, for the detection of most probable first heart sounds from several candidates. A final block is used for exact first heart sound timing and in turn foetal heart rate estimation. Filtering and enhancing blocks are actually implemented by means of different techniques, so that different processing paths are proposed. Furthermore, a reliability index is introduced to quantify the consistency of the estimated foetal heart rate and, based on statistic parameters; [,] a software quality index is designed to indicate the most reliable analysis procedure (that is, combining the best processing path and the most accurate time mark of the first heart sound, provides the lowest estimation errors). The algorithm performances have been tested on phonocardiographic signals recorded in a local gynaecology private practice from a sample group of about 50 pregnant women. Phonocardiographic signals have been recorded simultaneously to ultrasonic cardiotocographic signals in order to compare the two foetal heart rate series (the one estimated by our algorithm and the other provided by cardiotocographic device). Our results show that the proposed algorithm, in particular some analysis procedures, provides reliable foetal heart rate signals, very close to the reference cardiotocographic recordings.     

Hearing in honeybees: the mechanical response of the bee's antenna to near field sound

In the dance language, honeybees use airborne near field sound signals to inform their nestmates of the location of food sources. In behavioral experiments it has recently been shown that Johnston's organ, a chordotonal organ located in the pedicel of the antenna, is used to perceive these sound signals. In the present study the mechanical response of the antennal flagellum to stimulation with near field sound signals was investigated using laser vibrometry. The absolute amplitudes of antennal deflection with acoustical stimulation, the response to sounds of different displacement and velocity amplitudes, the shape of movement of the flagellum, the mechanical frequency response and the mechanical directional sensitivity of the auditory sense organ of the honeybee are described. Using pulsed stimuli simulating the dance sounds it is shown that the temporal pattern of the dance sound is resolved on the level of antennal vibrations.     

SOUND AND ITS SIGNIFICANCE FOR LABORATORY ANIMALS

1. Several methods of varying accuracy have been used to assess what sounds small laboratory animals such as rodents are capable of hearing. Most rodents can detect sounds from 1000 Hz (the frequency of the Greenwich Time Signal) up to 100000 Hz, depending on the strain, with usually one or more commonly two peaks of sensitivity within this range. Dogs can detect sound most easily from 500 Hz to 55000 Hz, depending on the breed. 2. Rodents also produce sound signals as a behavioural response and for communication in a variety of situations. Ultrasonic calls in the range 22000–70000 Hz are the main communicating pathway during aggressive encounters, mating, and mothering. Similar calls have also been recorded from isolated animals associated with inactivity, rest and possibly even sleep. 3. Very loud sounds cause seizures in rats and mice, or can make them more susceptible to other sounds later in life. This effect is possible even when animals are fully anaesthetized. Sound tends to startle and reduce activity in several species of animal. Even offspring of mice that have been sound-stressed exhibit abnormal behaviour patterns. Sounds also elicit various responses in rats from increasing aggression to making them more tolerant to electric shocks. 4. Levels of sound above 100 dB are teratogenic in several species of animals and several hormonal, haematological and reproductive parameters are disturbed by sounds above 80 dB. When rats are chemically deafened the disturbance to their fertility disappears. Lipid metabolism is disrupted in rats when exposed to over 95 dB of sounds, leading to increases in plasma triglycerides. Atherosclerosis can be produced in rabbits by similar levels of sound. 5. It has also been shown in guinea pigs and cats that hearing damage is governed by the duration as well as the intensity of the sound and is irreversible. Work on chinchillas hs demonstrated that sounds above 95 dB lead to this injury, but that sounds of 80 dB have no permanent effect on hearing sensitivity.     

Psychophysiology and psychoacoustics of music: Perception of complex sound in normal subjects and psychiatric patients

Perception of complex sound is a process carried out in everyday life situations and contributes in the way one perceives reality. Attempting to explain sound perception and how it affects human beings is complicated. Physics of simple sound can be described as a function of frequency, amplitude and phase. Psychology of sound, also termed psychoacoustics, has its own distinct elements of pitch, intensity and tibre. An interconnection exists between physics and psychology of hearing.Music being a complex sound contributes to communication and conveys information with semantic and emotional elements. These elements indicate the involvement of the central nervous system through processes of integration and interpretation together with peripheral auditory processing.Effects of sound and music in human psychology and physiology are complicated. Psychological influences of listening to different types of music are based on the different characteristics of basic musical sounds. Attempting to explain music perception can be simpler if music is broken down to its basic auditory signals. Perception of auditory signals is analyzed by the science of psychoacoustics. Differences in complex sound perception have been found between normal subjects and psychiatric patients and between different types of psychopathologies.     

Detection of baleen whale species using kernel dynamic mode decomposition-based feature extraction with a hidden Markov model

The negative effects of human activities within the ecological space of whales remains an issue of concern to marine ecologists. The accurate detection and subsequent classification of whale species are vital in mitigating these negative effects. Automatic detection techniques have come in handy for the efficient detection of the various whale species without human error. Hidden Markov model (HMM) remains one the most efficient detectors of whale species. However, its performance efficiency is greatly influenced by the feature vectors adapted with it. In this work, we propose the use of the kernel dynamic mode decomposition (kDMD) algorithm as a tool to extract features of baleen whale species, which are then adapted with HMM for their detection. Dynamic mode decomposition (DMD) is an eigendecomposition-based algorithm that is capable of extracting latent underlying features of non-linear signals such as those vocalised by whales. However, the underlying cost of DMD is the singular value decomposition (SVD), which adds significant complexity to the modes derivation steps. Thus, this work is introducing the kernel method into the DMD, in order to find a more efficient way of computing DMD without explicitly using the SVD algorithm. Furthermore, the feature formation steps in the original DMD was modified (mDMD) in this work, to make it more generic for datasets with sparse whale sound samples. The performance of the detectors was tested on datasets containing sounds of southern right whales (SRWs) and humpback whales. The results obtained show a high true positive rate (TPR), high precision (PREC) and low error rate (ERR) for both species. The performance of the three DMD-based feature-extraction methods were compared. The kDMD-HMM generally performed better than the mDMD-HMM and DMD-HMM detectors. The methods proposed here can be tailored for the automatic detection and classification of other vocalising animal species through their sounds.