Adaptive Efficient Coding of Correlated Acoustic Properties.

Natural sounds such as vocalizations often have covarying acoustic attributes, resulting in redundancy in neural coding. The efficient coding hypothesis proposes that sensory systems are able to detect such covariation and adapt to reduce redundancy, leading to more efficient neural coding. Recent psychoacoustic studies have shown the auditory system can rapidly adapt to efficiently encode two covarying dimensions as a single dimension, following passive exposure to sounds in which temporal and spectral attributes covaried in a correlated fashion. However, these studies observed a cost to this adaptation, which was a loss of sensitivity to the orthogonal dimension. Here we explore the neural basis of this psychophysical phenomenon by recording single-unit responses from the primary auditory cortex in awake ferrets exposed passively to stimuli with two correlated attributes, similar in stimulus design to the psychoacoustic experiments in humans. We found: (1) the signal-to-noise ratio of spike-rate coding of cortical responses driven by sounds with correlated attributes remained unchanged along the exposure dimension, but was reduced along the orthogonal dimension; (2) performance of a decoder trained with spike data to discriminate stimuli along the orthogonal dimension was equally reduced; (3) correlations between neurons tuned to the two covarying attributes decreased after exposure; and (4) these exposure effects still occurred if sounds were correlated along two acoustic dimensions, but varied randomly along a third dimension. These neurophysiological results are consistent with the efficient coding hypothesis and may help deepen our understanding of how the auditory system encodes and represents acoustic regularities and covariance.SIGNIFICANCE STATEMENT The efficient coding (EC) hypothesis (Attneave, 1954; Barlow, 1961) proposes that the neural code in sensory systems efficiently encodes natural stimuli by minimizing the number of spikes to transmit a sensory signal. Results of recent psychoacoustic studies in humans are consistent with the EC hypothesis in that, following passive exposure to stimuli with correlated attributes, the auditory system rapidly adapts so as to more efficiently encode the two covarying dimensions as a single dimension. In the current neurophysiological experiments, using a similar stimulus design and the experimental paradigm to the psychoacoustic studies of Stilp et al. (2010) and Stilp and Kluender (2011, 2012, 2016), we recorded responses from single neurons in the auditory cortex of the awake ferret, showing adaptive efficient neural coding of two correlated acoustic attributes.

Exaggerated cortical representation of speech in older listeners: mutual information analysis.

Aging is associated with an exaggerated representation of the speech envelope in auditory cortex. The relationship between this age-related exaggerated response and a listener's ability to understand speech in noise remains an open question. Here, information-theory-based analysis methods are applied to magnetoencephalography recordings of human listeners, investigating their cortical responses to continuous speech, using the novel nonlinear measure of phase-locked mutual information between the speech stimuli and cortical responses. The cortex of older listeners shows an exaggerated level of mutual information, compared with younger listeners, for both attended and unattended speakers. The mutual information peaks for several distinct latencies: early (∼50 ms), middle (∼100 ms), and late (∼200 ms). For the late component, the neural enhancement of attended over unattended speech is affected by stimulus signal-to-noise ratio, but the direction of this dependency is reversed by aging. Critically, in older listeners and for the same late component, greater cortical exaggeration is correlated with decreased behavioral inhibitory control. This negative correlation also carries over to speech intelligibility in noise, where greater cortical exaggeration in older listeners is correlated with worse speech intelligibility scores. Finally, an age-related lateralization difference is also seen for the ∼100 ms latency peaks, where older listeners show a bilateral response compared with younger listeners' right lateralization. Thus, this information-theory-based analysis provides new, and less coarse-grained, results regarding age-related change in auditory cortical speech processing, and its correlation with cognitive measures, compared with related linear measures.NEW & NOTEWORTHY Cortical representations of natural speech are investigated using a novel nonlinear approach based on mutual information. Cortical responses, phase-locked to the speech envelope, show an exaggerated level of mutual information associated with aging, appearing at several distinct latencies (∼50, ∼100, and ∼200 ms). Critically, for older listeners only, the ∼200 ms latency response components are correlated with specific behavioral measures, including behavioral inhibition and speech comprehension.

Implicit Memory for Complex Sounds in Higher Auditory Cortex of the Ferret.

Responses of auditory cortical neurons encode sound features of incoming acoustic stimuli and also are shaped by stimulus context and history. Previous studies of mammalian auditory cortex have reported a variable time course for such contextual effects ranging from milliseconds to minutes. However, in secondary auditory forebrain areas of songbirds, long-term stimulus-specific neuronal habituation to acoustic stimuli can persist for much longer periods of time, ranging from hours to days. Such long-term habituation in the songbird is a form of long-term auditory memory that requires gene expression. Although such long-term habituation has been demonstrated in avian auditory forebrain, this phenomenon has not previously been described in the mammalian auditory system. Utilizing a similar version of the avian habituation paradigm, we explored whether such long-term effects of stimulus history also occur in auditory cortex of a mammalian auditory generalist, the ferret. Following repetitive presentation of novel complex sounds, we observed significant response habituation in secondary auditory cortex, but not in primary auditory cortex. This long-term habituation appeared to be independent for each novel stimulus and often lasted for at least 20 min. These effects could not be explained by simple neuronal fatigue in the auditory pathway, because time-reversed sounds induced undiminished responses similar to those elicited by completely novel sounds. A parallel set of pupillometric response measurements in the ferret revealed long-term habituation effects similar to observed long-term neural habituation, supporting the hypothesis that habituation to passively presented stimuli is correlated with implicit learning and long-term recognition of familiar sounds.SIGNIFICANCE STATEMENT Long-term habituation in higher areas of songbird auditory forebrain is associated with gene expression and is correlated with recognition memory. Similar long-term auditory habituation in mammals has not been previously described. We studied such habituation in single neurons in the auditory cortex of awake ferrets that were passively listening to repeated presentations of various complex sounds. Responses exhibited long-lasting habituation (at least 20 min) in the secondary, but not primary auditory cortex. Habituation ceased when stimuli were played backward, despite having identical spectral content to the original sound. This long-term neural habituation correlated with similar habituation of ferret pupillary responses to repeated presentations of the same stimuli, suggesting that stimulus habituation is retained as a long-term behavioral memory.

Mutual information analysis of neural representations of speech in noise in the aging midbrain.

Younger adults with normal hearing can typically understand speech in the presence of a competing speaker without much effort, but this ability to understand speech in challenging conditions deteriorates with age. Older adults, even with clinically normal hearing, often have problems understanding speech in noise. Earlier auditory studies using the frequency-following response (FFR), primarily believed to be generated by the midbrain, demonstrated age-related neural deficits when analyzed with traditional measures. Here we use a mutual information paradigm to analyze the FFR to speech (masked by a competing speech signal) by estimating the amount of stimulus information contained in the FFR. Our results show, first, a broadband informational loss associated with aging for both FFR amplitude and phase. Second, this age-related loss of information is more severe in higher-frequency FFR bands (several hundred hertz). Third, the mutual information between the FFR and the stimulus decreases as noise level increases for both age groups. Fourth, older adults benefit neurally, i.e., show a reduction in loss of information, when the speech masker is changed from meaningful (talker speaking a language that they can comprehend, such as English) to meaningless (talker speaking a language that they cannot comprehend, such as Dutch). This benefit is not seen in younger listeners, which suggests that age-related informational loss may be more severe when the speech masker is meaningful than when it is meaningless. In summary, as a method, mutual information analysis can unveil new results that traditional measures may not have enough statistical power to assess.NEW & NOTEWORTHY Older adults, even with clinically normal hearing, often have problems understanding speech in noise. Auditory studies using the frequency-following response (FFR) have demonstrated age-related neural deficits with traditional methods. Here we use a mutual information paradigm to analyze the FFR to speech masked by competing speech. Results confirm those from traditional analysis but additionally show that older adults benefit neurally when the masker changes from a language that they comprehend to a language they cannot.

Cyclin D1 Gene Silencing Promotes IL-1ß-Induced Apoptosis in Rat Chondrocytes.

This study investigated the effects of cyclin D1 gene silencing on cell proliferation and apoptosis of interleukin-1ß (IL-1ß)-induced osteoarthritis (OA) chondrocytes. Chondrocytes from healthy sprague-dawley rats were divided into blank, OA model (chondrocytes underwent IL-1ß inducement), OA trial (chondrocytes underwent IL-1ß inducement with cyclin D1-shRNA treatment), and negative control (NC; chondrocytes underwent IL-1ß inducement and control-shRNA treatment) groups. Cell proliferation was assessed by CCK-8 assay, and cell cycle and apoptosis by flow cytometry. qRT-PCR and Western blotting were performed to detect cyclin D1 and apoptosis-related factors expression levels. Chondrocyte proliferation increased after 72-96 h after incubation. The OA trial group exhibited reduced cell proliferation at 48, 72, and 96 h after treatment. The OA model, OA trial, and NC groups all contained more cells arrested in G1 phase and had higher apoptosis rates than the blank group. Additionally, the OA trial group contained more cells arrested in G1 phase, with increased apoptosis rates compared to the OA model and NC groups. The OA model group had lowest expression of cyclin D1 whereas the blank group contained the highest among the four groups. qRT-PCR also showed that the OA model, OA trial, and NC groups all had increased expression levels of Bax and reduced expression levels of Bcl-2 and P53 compared to the blank group, whereby by the OA group had the most significant change. The combined evidence in our study shows that cyclin D1 gene silencing suppresses proliferation and induces apoptosis of rat chondrocytes in IL-1ß-induced OA. J. Cell. Biochem. 119: 290-299, 2018. © 2017 Wiley Periodicals, Inc.

Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.

Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.

A regression method for EEG-based cross-dataset fatigue detection.

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model. Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information. Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods. Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices.

A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection.

Fatigue, one of the most important factors affecting road safety, has attracted many researchers' attention. Most existing fatigue detection methods are based on feature engineering and classification models. The feature engineering is greatly influenced by researchers' domain knowledge, which will lead to a poor performance in fatigue detection, especially in cross-subject experiment design. In addition, fatigue detection is often simplified as a classification problem of several discrete states. Models based on deep learning can realize automatic feature extraction without the limitation of researcher's domain knowledge. Therefore, this paper proposes a regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based (EEG-based) cross-subject fatigue detection. At the same time, a twofold random-offset zero-overlapping sampling method is proposed to train a bigger model and reduce overfitting. Compared with existing results, the proposed method achieves a much better result of 0.94 correlation coefficient (COR) and 0.09 root mean square error (RMSE) in a within-subject experiment design. What is more, there is no misclassification between awake and drowsy states. For cross-subject experiment design, the COR and RMSE are 0.79 and 0.15, respectively, which are close to the existing within-subject results and better than similar cross-subject results. The cross-subject regression model is very important for fatigue detection application since the fatigue indication is more precise than several discrete states and no model calibration is required for a new user. The twofold random-offset zero-overlapping sampling method can also be used as a reference by other EEG-based deep learning research.

Cortical connectomic mediations on gamma band synchronization in schizophrenia.

Aberrant gamma frequency neural oscillations in schizophrenia have been well demonstrated using auditory steady-state responses (ASSR). However, the neural circuits underlying 40 Hz ASSR deficits in schizophrenia remain poorly understood. Sixty-six patients with schizophrenia spectrum disorders and 85 age- and gender-matched healthy controls completed one electroencephalography session measuring 40 Hz ASSR and one imaging session for resting-state functional connectivity (rsFC) assessments. The associations between the normalized power of 40 Hz ASSR and rsFC were assessed via linear regression and mediation models. We found that rsFC among auditory, precentral, postcentral, and prefrontal cortices were positively associated with 40 Hz ASSR in patients and controls separately and in the combined sample. The mediation analysis further confirmed that the deficit of gamma band ASSR in schizophrenia was nearly fully mediated by three of the rsFC circuits between right superior temporal gyrus-left medial prefrontal cortex (MPFC), left MPFC-left postcentral gyrus (PoG), and left precentral gyrus-right PoG. Gamma-band ASSR deficits in schizophrenia may be associated with deficient circuitry level connectivity to support gamma frequency synchronization. Correcting gamma band deficits in schizophrenia may require corrective interventions to normalize these aberrant networks.

Effects of soil fauna on litter decomposition in Chinese forests: a meta-analysis.

Litter quality and climate have been presumed to be the dominant factors regulating litter decomposition rates on broad spatial scales. However, the role of soil fauna on litter decomposition is poorly understood, despite the fact that it could strongly influence decomposition by fragmentation and subsequent modification of the activities of microorganisms.In this study, we carried out a meta-analysis on the effects of soil fauna on litter decomposition rates in Chinese forests, ranging from boreal to tropical forests, based on data from 20 studies. The effects of climatic factors on decomposition rate were assessed by comparing the contribution of soil fauna to litter decomposition from studies carried out at different latitudes.The degree of influence of the soil fauna was in the order tropical (200%) > subtropical (47%) > temperate forest (28%). Comparing the effect size of soil fauna, it was found that when soil fauna was excluded, the decomposition rate, calculated using Olson's equation, was most affected in tropical forest (-0.77), while the litter decomposition rate both subtropical (-0.36) and temperate forest (-0.19) were also suppressed to varying degrees (P < 0.001). These results highlight that soil fauna could promote litter decomposition to different extents. Using stepwise multiple linear regression, the effect size of the soil fauna was negatively correlated with the cellulose and nitrogen concentrations of the initial litter material. In Chinese forests, litter decomposition rates were reduced, on average, by 65% when soil fauna was excluded. The impact of soil fauna on decomposition was shown to be closely related to climate and litter quality.

Research on improved intestinal image classification for LARS based on ResNet.

Low anterior rectal resection is an effective way to treat rectal cancer at present, but it is easy to cause low anterior resection syndrome after surgery; so, a comprehensive diagnosis of defecation and pelvic floor function must be carried out. There are few studies on the classification of diagnoses in the field of intestinal diseases. In response to these outstanding problems, this research will focus on the design of the intestinal function diagnosis system and the image processing and classification algorithm of the intestinal wall to verify an efficient fusion method, which can be used to diagnose the intestinal diseases in clinical medicine. The diagnostic system designed in this paper makes up for the singleness of clinical monitoring methods. At the same time, the Res-SVDNet neural network model is used to solve the problems of small intestinal image samples and network overfitting, and achieve efficient fusion diagnosis of intestinal diseases in patients. Different models were used to compare experiments on the constructed datasets to verify the applicability of the Res-SVDNet model in intestinal image classification. The accuracy of the model was 99.54%, which is several percentage points higher than other algorithm models.

Diagnosis analysis of rectal function through using ensemble empirical mode decomposition-deep belief networks algorithm.

The rectal motility function can reflect a person's rectal health status. To diagnose the rectal motility function after artificial anal sphincter implantation, this paper proposes a rectal function diagnosis model based on ensemble empirical mode decomposition-deep belief networks (EEMD-DBNs). Because of the rectal pressure signals that are unstable and subjected to noise interferences, an EEMD framework based on EMD, which can reduce the effect of signal modal mixing, is proposed. EMD and EEMD were used to decompose the analog signal, respectively, and it was found that EEMD can significantly reduce the effect of mode aliasing. During the rectal pressure signal decomposition experiment, by analyzing the intrinsic mode functions generated by the signals from normal people and diseased patients, the rectal signals at these two different conditions can be well distinguished. Additionally, the DBN was introduced to perform deep learning to extract the multi-dimensional features of rectal signals and then output the classification results via using the top-level classifier, which can overcome the difficulties in extracting the rectal signal features. The results showed that, following the principle of balancing the diagnosis accuracy and model running speed, the best diagnosis performance was achieved when three restricted Boltzmann machines and five layers of DBN model were set, with the diagnosis rate of 85%. The diagnostic model used in this study can distinguish the signals between normal and abnormal rectal functions with accurate performance, thus providing the technical support for the recovery of the rectal motility function of artificial anal sphincter implanters.

A new type of medical micropump for an endoscopic robot.

Researchers have developed a new type of medical micropump for an endoscopic robot, which is driven by a linear actuator based on a direct current (DC) motor. This micropump consists of two active one-way valves and a cylindrical air drum. The overall size of the pump prototype is 12.5 mm in diameter and 56 mm in length. This paper describes the structure of the micropump and linear actuator and analyzes the inflation mechanism of the micropump. The experimental results show that the driving force of the linear actuator can reach up to 2.55 N, which fulfills the need of the micropump. The rated output flow of the micropump is 16 mL/min, which can rapidly supply the gas bag with enough air with minimal noise and vibration.

Biomechanical modeling of the rectum for the design of a novel artificial anal sphincter.

This paper discusses biomechanical issues that are related to the option of a novel artificial anal sphincter around the human rectum. The prosthesis consists of a compression cuff system inside and a reservoir cuff system outside, which is placed around the debilitated sphincter muscle. The micropump shifts fluid between the cuffs and thus takes over the expansion and compression function of the sphincter muscle. However, the human rectum is not a rigid pipe, and motion in it is further complicated by the fact that the bowel is susceptible to damage. With the goal of engineering a safe and reliable machine, the biomechanical properties of the in-vivo porcine rectum are studied and the tissue ischemia is analyzed.

Study on gastric interdigestive pressure activity based on phase space reconstruction and FastICA algorithm.

To investigate the features of the gastric interdigestive pressure activity under normal physiological conditions, we have developed the wireless radiotelemetry capsule based on a telemetry technique. Twelve healthy volunteers participated in this study. Pressure activity data which are an important index of gastric motility can be obtained from the wireless radiotelemetry capsule. But the capsule only records single-dimensional pressure time series which may contain a few interdependent components simultaneously. Automated embedding phase space reconstruction algorithm is employed to reconstruct multi-dimensional phase space. Then the dominant and separated component of the gastric contractions is identified using FastICA algorithm. Finally the use of Hilbert Huang transform method for analyzing the characters of gastric motility is investigated. The results show that the proposed method is an effective approach for the analysis of the gastric pressure series.

Research of improved fast independent component analysis algorithm in rectal diagnosis signal preprocessing.

With the maturity of artificial organ technology, the use of artificial anal sphincters was proposed to help patients who suffered anal incontinence for various causes reconstruct rectal perception, monitor rectal pressure and diagnose rectal lesion. Aimed at the lack of signal pretreatment in the artificial anal sphincter system, we find a way to solve it, that is, the multi-dimensional reconstruction of the intestinal one-dimensional pressure signal sequence by using phase space reconstruction, and the separation of the reconstructed signal by using the improved fast independent component analysis algorithm. We did some relevant experiments, further extracted the features of the isolated rectal signal, and used back propagation neural network to diagnose the rectal lesions. Experiments show that the method can pretreat the rectal signal, and further analyze the separated signal to diagnose of rectal function. The improved fast independent component analysis algorithm has few iterations, fast convergence, short run time, low requirements on initial weights and good diagnosis. This study lays a foundation for the diagnosis of rectal function by using artificial anal sphincters.

Modeling of human colonic blood flow for a novel artificial anal sphincter system.

A novel artificial anal sphincter system has been developed to simulate the normal physiology of the human anorectum. With the goal of engineering a safe and reliable device, the model of human colonic blood flow has been built and the relationship between the colonic blood flow rate and the operating occlusion pressure of the anorectum is achieved. The tissue ischemia is analyzed based on constitutive relations for human anorectum. The results suggest that at the planned operating occlusion pressure of less than 4 kPa the artificial anal sphincter should not risk the vascularity of the human colon.

New KF-PP-SVM classification method for EEG in brain-computer interfaces.

Classification methods are a crucial direction in the current study of brain-computer interfaces (BCIs). To improve the classification accuracy for electroencephalogram (EEG) signals, a novel KF-PP-SVM (kernel fisher, posterior probability, and support vector machine) classification method is developed. Its detailed process entails the use of common spatial patterns to obtain features, based on which the within-class scatter is calculated. Then the scatter is added into the kernel function of a radial basis function to construct a new kernel function. This new kernel is integrated into the SVM to obtain a new classification model. Finally, the output of SVM is calculated based on posterior probability and the final recognition result is obtained. To evaluate the effectiveness of the proposed KF-PP-SVM method, EEG data collected from laboratory are processed with four different classification schemes (KF-PP-SVM, KF-SVM, PP-SVM, and SVM). The results showed that the overall average improvements arising from the use of the KF-PP-SVM scheme as opposed to KF-SVM, PP-SVM and SVM schemes are 2.49%, 5.83 % and 6.49 % respectively.

Assessment and in vitro experiment of artificial anal sphincter system based on rebuilding the rectal sensation function.

In this paper, a novel artificial anal sphincter (AAS) system based on rebuilding the rectal sensation function is proposed to treat human fecal incontinence. The executive mechanism of the traditional AAS system was redesigned and integrated for a simpler structure and better durability. The novel executive mechanism uses a sandwich structure to simulate the basic function of the natural human anal sphincter. To rebuild the lost rectal sensation function caused by fecal incontinence, we propose a novel method for rebuilding the rectal sensation function based on an Optimal Wavelet Packet Basis (OWPB) using the Davies-Bouldin (DB) index and a support vector machine (SVM). OWPB using a DB index is used for feature vector extraction, while a SVM is adopted for pattern recognition.Furthermore, an in vitro experiment with the AAS system based on rectal sensation function rebuilding was carried out. Experimental results indicate that the novel executive mechanism can simulate the basic function of the natural human anal sphincter, and the proposed method is quite effective for rebuilding rectal sensation in patients.

Electromagnetic assessment of embedded micro antenna for a novel sphincter in the human body.

This paper presents a wireless, miniaturized, bi-directional telemetric artificial anal sphincter system that can be used for controlling patients' anal incontinence. The artificial anal sphincter system is mainly composed of an executive mechanism, a wireless power supply system and a wireless communication system. The wireless communication system consists of an internal RF transceiver, an internal RF antenna, a data transmission pathway, an external RF antenna and an external RF control transceiver. A micro NMHA (Normal Mode Helical Antenna) has been used for the transceiver of the internal wireless communication system and a quarter wave-length whip antenna of 7.75 cm has been used for the external wireless communication system. The RF carrier frequency of wireless communication is located in a license-free 433.1 MHz ISM (Industry, Science, and Medical) band. The radiation characteristics and SAR (Specific Absorption Rate) are evaluated using the finite difference time-domain method and 3D human body model. Results show that the SAR values of the antenna satisfy the ICNIRP (International Commission on Nonionizing Radiation Protection) limitations.