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Recently, Computer Aided Diagnosis (CAD) systems based on Convolutional Neural Networks (CNNs) showed promising performance in Alzheimer’s Disease (AD) diagnosis using imaging bio-markers. However, the detection of early AD stages namely Mild Cognitive Impairment (MCI) and Mild Alzheimer Disease (MAD) remains hard to achieve using imaging features. Proton Magnetic Resonance Spectroscopy (${ }^{1}$H-MRS), a powerful non-invasive technique for early diseases diagnosis, provides a promising solution for early biological brain changes detection. In this paper, we propose an explainable classification framework for early AD detection using ${ }^{1}$H-MRS. The proposed method consists of an end-to-end One Dimensional-CNN model integrating a novel decision interpretation method. Data used in this paper are collected in the University Hospital of Poitiers, which contain 111 ${ }^{1}$H-MRS samples divided into 3 classes namely Normal Control (NC), MCI and MAD. The proposed framework achieves an accuracy of $82\%$ between for the most challenging classification task (MCI vs. MAD classification). Yet, the proposed AD detection framework is explainable, highlighting the clinically relevant brain metabolites for early AD subjects discrimination.