利用静态小波熵检测单侧听力损失。
Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy.
机构信息
Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, Changsha, Hunan 410138, China.
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China.
出版信息
CNS Neurol Disord Drug Targets. 2017;16(2):122-128. doi: 10.2174/1871527315666161026115046.
AIM
Sensorineural hearing loss is correlated to massive neurological or psychiatric disease.
MATERIALS
T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC).
METHOD
We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier.
RESULTS
The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively.
CONCLUSION
Our developed system is promising and effective in detecting hearing loss.
目的
感音神经性听力损失与大量神经或精神疾病相关。
材料
从 14 名右侧听力损失（RHL）患者、15 名左侧听力损失（LHL）患者和 20 名健康对照者（HC）中获得 T1 加权容积图像。
方法
我们处理了一个三分类问题：HC、LHL 和 RHL。采用稳态小波熵从每位受试者的磁共振图像中提取全局特征。这些稳态小波熵特征被用作单个隐藏层前馈神经网络分类器的输入。
结果
10 折交叉验证的 10 次重复结果表明，HC、LHL 和 RHL 的准确率分别为 96.94%、97.14%和 97.35%。
结论
我们开发的系统在检测听力损失方面具有很大的潜力和有效性。
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