# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 2 Early Diseases Detection through Physiological Characteristics
### Abnormal Localization  
As an important physiological characteristic, the vocalization of poultry is accepted as an indicator of health and welfare [31; 32]. It has been successfully used in the prediction of poultry weight and analysis of pecking activity, which can indirectly reflect the health status of chicken [33; 34; 35; 36; 37]. In addition, vocalization is generated by specific organs and is an expression of the physiological signal caused by internal or external factors, which is closely related to the health of the respiratory system. The difference in the vocalization of chickens may be to alert the breeder to pay attention to poor environments or disease infection. Hence, it makes sense to regard vocalization as an early indicator of animals' health. Rales, an adventitious sound besides the breathing sound, is one of the common symptoms of respiratory diseases in poultry. [PERSON] et al. [38] infected six fifteen-day-old chickens with infectious bronchitis virus, and recorded the sound of healthy and infected chickens with a microphone, respectively. In their study, the Mel Frequency Cepstral Coefficients (MFCCs), which is the most widely used feature for speech recognition and can represent the spectrum of animal sounds in a compact form [39], were calculated and input to the C4.5 decision tree to classify the sound of healthy and infected chickens, and the classification accuracy was 73.4%. Decision tree is a kind of sequential model, which has been widely used to build classification models, as such models closely resemble human reasoning and are easy to understand [40]. For the same sound data, [PERSON] et al. [41] applied dictionary learning and sparse coding methods to classify sound signals, and the accuracy was improved to 97.85%, and [PERSON] et al. [42] developed an extreme learning machine (ELM) and support vector machine (SVM) classifiers to detect rules, and their accuracy was 97.1% and 97.6%, respectively. [PERSON] et al. [43] have shown that sound signals from poultry infected with different diseases would exhibit different acoustic characteristics that could be distinguished. They divided 308 fourteen-day-old Ross chickens into four groups, with 60 chickens in each group. The first group was considered as the control group (without infection). The second, third and fourth groups were infected with Newcastle disease (ND), infectious bronchitis disease (IBD) and avian influenza (AI), respectively. Figure 3 shows the time domain of the chicken's sound signals of four classes of healthy, ND, IBD and AI chickens [43]. Then, they combined SVM and Dempster-Shafer evidence theory to identify the four classes of sound data, and an accuracy of 91.15% was finally achieved. Sound samples and results from the above studies were obtained in a closed environment, which means there is no ambient noises interference, but this is not consistent with the actual situation of commercial farms. Sneezing is a clinical sign of many respiratory diseases. In order to detect sneezing sounds in a situation where multiple noise sources are present, an algorithm was developed and implemented with a sensitivity of 66.7% and an accuracy of 88.4%, which demonstrated the feasibility of an automated sound-based monitoring system for poultry health [44]. [PERSON] et al. [45] extracted five acoustic features (MFCCs, energy of Mel sub-bands, wavelet energy, wavelet entropy and spectral flatness) from healthy and sick chickens for sound classification, and they found that wavelet energy and MFCCs were able to detect Newcastle disease on the fourth day after inoculation with an accuracy of 80% and 78%, respectively. Althexhibited a similar accuracy, wavelet energy had better performance in detecting healthy chickens while MFCCs were better at detecting challenged birds. The acoustic features presented by chickens with different diseases usually cannot be described by the same set of features, resulting in the low generalization performance of the model established by artificial feature engineering.