WAVELET TRANSFORM APPLICATIONS TO BIOLOGY

有许多生物数据或信号的实例是由 自然非平稳，如脑电和心电。通常，目标是分析 或对数据进行量化评估。因为 数据的非平稳性质，它在不同大小尺度上的存在，以及 希望以高效的计算方式定位要素，它 重要的是我们使用小波变换来处理数据 恰如其分。此转换的优点是表示 数据的频率内容及其发生的时间。 此外，小波变换具有提取特征的能力 各种比例，以增强边缘，并放大奇点。我们 将小波变换应用于脑电信号的分析，并对其进行了分析 检测尖峰、振荡和癫痫发作。我们是 目前正在研究它在估计大气中的分维方面的应用 定量测量神经胶质细胞的形态复杂性。

There are many instances of biological data or signals that are by nature nonstationary, e.g., EEG and ECG. Often the goal is to analyze the data or to evaluate the data quantitatively. Because of the data's nonstationary nature, its existence at various size scales, and the desire to locate features in a computationally efficient way, it is important that we use the wavelet transform to process the data appropriately. This transform has the advantage of representing the frequency content of the data and the time at which they occurred. Moreover, the wavelet transform has the ability to extract features at various scales, to enhance edges, and to zoom in on singularities. We have applied the wavelet transform to analyze EEG signals and to detect spikes, oscillations, and the onset of seizures. We are currently investigating its use in estimating the fractal dimension of glial nerve cells to quantitatively measure morphological complexity.